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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1211.0974 | Paul Sniegowski | Philip Gerrish, Alexandre Colato, Paul Sniegowski | Genomic mutation rates that neutralize adaptive evolution and natural
selection | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When mutation rates are low, natural selection remains effective, and
increasing the mutation rate can give rise to an increase in adaptation rate.
When mutation rates are high to begin with, however, increasing the mutation
rate may have a detrimental effect because of the overwhelming presence of
deleterious mutations. Indeed, if mutation rates are high enough: 1) adaptation
rate can become negative despite the continued availability of adaptive and/or
compensatory mutations, or 2) natural selection may be disabled because
adaptive and/or compensatory mutations -- whether established or newly-arising
-- are eroded by excessive mutation and decline in frequency. We apply these
two criteria to a standard model of asexual adaptive evolution and derive
mathematical expressions -- some new, some old in new guise -- delineating the
mutation rates under which either adaptive evolution or natural selection is
neutralized. The expressions are simple and require no \emph{a priori}
knowledge of organism- and/or environment-specific parameters. Our discussion
connects these results to each other and to previous theory, showing
convergence or equivalence of the different results in most cases.
| [
{
"created": "Mon, 5 Nov 2012 19:05:52 GMT",
"version": "v1"
}
] | 2012-11-06 | [
[
"Gerrish",
"Philip",
""
],
[
"Colato",
"Alexandre",
""
],
[
"Sniegowski",
"Paul",
""
]
] | When mutation rates are low, natural selection remains effective, and increasing the mutation rate can give rise to an increase in adaptation rate. When mutation rates are high to begin with, however, increasing the mutation rate may have a detrimental effect because of the overwhelming presence of deleterious mutations. Indeed, if mutation rates are high enough: 1) adaptation rate can become negative despite the continued availability of adaptive and/or compensatory mutations, or 2) natural selection may be disabled because adaptive and/or compensatory mutations -- whether established or newly-arising -- are eroded by excessive mutation and decline in frequency. We apply these two criteria to a standard model of asexual adaptive evolution and derive mathematical expressions -- some new, some old in new guise -- delineating the mutation rates under which either adaptive evolution or natural selection is neutralized. The expressions are simple and require no \emph{a priori} knowledge of organism- and/or environment-specific parameters. Our discussion connects these results to each other and to previous theory, showing convergence or equivalence of the different results in most cases. |
2311.14815 | Yunyi Shen | Yunyi Shen and Erik R. Olson and Timothy R. Van Deelen | Geometric theory on large-scale and local determination of density
dependence of a recovering large carnivore population | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-sa/4.0/ | Density-dependent population growth is a feature of large carnivores like
wolves ($\textit{Canis lupus}$), with mechanisms typically attributed to
resource (e.g. prey) limitation. Such mechanisms are local phenomena and rely
on individuals having access to information, such as prey availability at their
location. Using over four decades of wolf population and range expansion data
from Wisconsin (USA) wolves, we found that the population not only exhibited
density dependence locally but also at landscape scale. Superficially, one may
consider space as yet another limiting resource to explain landscape-scale
density dependence. However, this view poses an information puzzle: most
individuals do not have access to global information such as range-wide habitat
availability as they would for local prey availability. How would the
population "know" when to slow their range expansion? To understand observed
large-scale spatial density dependence, we propose a reaction-diffusion model,
first introduced by Fisher and Kolmogorov, with a "travelling wave" solution,
wherein the population expands from a core range that quickly achieves local
carrying capacity. Early-stage acceleration and later-stage deceleration of
population growth can be explained by early elongation of an expanding frontier
and a later collision of the expanding frontier with a habitat boundary. Such a
process does not require individuals to have global density information. We
illustrate our proposal with simulations and spatial visualizations of wolf
recolonization in the western Great Lakes region over time relative to habitat
suitability. We further synthesize previous studies on wolf habitat selection
in the western Great Lakes region and argue that the habitat boundary appeared
to be driven by spatial variation in mortality, likely associated with human
use of the landscape.
| [
{
"created": "Fri, 24 Nov 2023 19:18:43 GMT",
"version": "v1"
}
] | 2023-11-28 | [
[
"Shen",
"Yunyi",
""
],
[
"Olson",
"Erik R.",
""
],
[
"Van Deelen",
"Timothy R.",
""
]
] | Density-dependent population growth is a feature of large carnivores like wolves ($\textit{Canis lupus}$), with mechanisms typically attributed to resource (e.g. prey) limitation. Such mechanisms are local phenomena and rely on individuals having access to information, such as prey availability at their location. Using over four decades of wolf population and range expansion data from Wisconsin (USA) wolves, we found that the population not only exhibited density dependence locally but also at landscape scale. Superficially, one may consider space as yet another limiting resource to explain landscape-scale density dependence. However, this view poses an information puzzle: most individuals do not have access to global information such as range-wide habitat availability as they would for local prey availability. How would the population "know" when to slow their range expansion? To understand observed large-scale spatial density dependence, we propose a reaction-diffusion model, first introduced by Fisher and Kolmogorov, with a "travelling wave" solution, wherein the population expands from a core range that quickly achieves local carrying capacity. Early-stage acceleration and later-stage deceleration of population growth can be explained by early elongation of an expanding frontier and a later collision of the expanding frontier with a habitat boundary. Such a process does not require individuals to have global density information. We illustrate our proposal with simulations and spatial visualizations of wolf recolonization in the western Great Lakes region over time relative to habitat suitability. We further synthesize previous studies on wolf habitat selection in the western Great Lakes region and argue that the habitat boundary appeared to be driven by spatial variation in mortality, likely associated with human use of the landscape. |
1703.09581 | D\'esir\'e Ou\'edraogo | Tom Britton and D\'esir\'e Ou\'edraogo | SEIRS epidemics in growing populations | null | null | null | null | q-bio.PE math.DS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An SEIRS epidemic with disease fatalities is introduced in a growing
population (modelled as a super-critical linear birth and death process). The
study of the initial phase of the epidemic is stochastic, while the analysis of
the major outbreaks is deterministic. Depending on the values of the
parameters, the following scenarios are possible. i) The disease dies out
quickly, only infecting few; ii) the epidemic takes off, the \textit{number} of
infected individuals grows exponentially, but the \textit{fraction} of infected
individuals remains negligible; iii) the epidemic takes off, the
\textit{number} of infected grows initially quicker than the population, the
disease fatalities diminish the growth rate of the population, but it remains
super critical, and the \emph{fraction} of infected go to an endemic
equilibrium; iv) the epidemic takes off, the \textit{number} of infected
individuals grows initially quicker than the population, the diseases
fatalities turn the exponential growth of the population to an exponential
decay.
| [
{
"created": "Tue, 28 Mar 2017 14:05:28 GMT",
"version": "v1"
}
] | 2017-03-29 | [
[
"Britton",
"Tom",
""
],
[
"Ouédraogo",
"Désiré",
""
]
] | An SEIRS epidemic with disease fatalities is introduced in a growing population (modelled as a super-critical linear birth and death process). The study of the initial phase of the epidemic is stochastic, while the analysis of the major outbreaks is deterministic. Depending on the values of the parameters, the following scenarios are possible. i) The disease dies out quickly, only infecting few; ii) the epidemic takes off, the \textit{number} of infected individuals grows exponentially, but the \textit{fraction} of infected individuals remains negligible; iii) the epidemic takes off, the \textit{number} of infected grows initially quicker than the population, the disease fatalities diminish the growth rate of the population, but it remains super critical, and the \emph{fraction} of infected go to an endemic equilibrium; iv) the epidemic takes off, the \textit{number} of infected individuals grows initially quicker than the population, the diseases fatalities turn the exponential growth of the population to an exponential decay. |
1801.01362 | Ulisse Ferrari | Ulisse Ferrari, Stephane Deny, Olivier Marre, Thierry Mora | A simple model for low variability in neural spike trains | null | null | null | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural noise sets a limit to information transmission in sensory systems. In
several areas, the spiking response (to a repeated stimulus) has shown a higher
degree of regularity than predicted by a Poisson process. However, a simple
model to explain this low variability is still lacking. Here we introduce a new
model, with a correction to Poisson statistics, which can accurately predict
the regularity of neural spike trains in response to a repeated stimulus. The
model has only two parameters, but can reproduce the observed variability in
retinal recordings in various conditions. We show analytically why this
approximation can work. In a model of the spike emitting process where a
refractory period is assumed, we derive that our simple correction can well
approximate the spike train statistics over a broad range of firing rates. Our
model can be easily plugged to stimulus processing models, like
Linear-nonlinear model or its generalizations, to replace the Poisson spike
train hypothesis that is commonly assumed. It estimates the amount of
information transmitted much more accurately than Poisson models in retinal
recordings. Thanks to its simplicity this model has the potential to explain
low variability in other areas.
| [
{
"created": "Thu, 4 Jan 2018 14:17:15 GMT",
"version": "v1"
}
] | 2018-01-08 | [
[
"Ferrari",
"Ulisse",
""
],
[
"Deny",
"Stephane",
""
],
[
"Marre",
"Olivier",
""
],
[
"Mora",
"Thierry",
""
]
] | Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, which can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters, but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like Linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity this model has the potential to explain low variability in other areas. |
1608.02752 | Abderrahim Chafik Dr. | Abderrahim Chafik | The role of CRKL in Breast Cancer Metastasis: Insights from Systems
Biology | 10 pages, 2 figures | Systems and Synthetic Biology Volume 9, Issue 4 (2015), Page
141-146 | 10.1007/s11693-015-9180-z | null | q-bio.MN | http://creativecommons.org/publicdomain/zero/1.0/ | MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression
post-transcriptionally. They are involved in key biological processes and then
may play a major role in the development of human diseases including cancer, in
particular their involvement in breast cancer metastasis has been confirmed.
Recently, the authors of ref.(\cite{key1} have found that miR-429 may have a
role in the inhibition of breast cancer metastasis and have identified its
target gene CRKL as a potential candidate. In this paper, by using systems
biology tools we have shown that CRKL is involved in positive regulation of
ERK1/2 signaling pathway and contribute to the regulation of LYN through a
topological generalization of feed forward loop.
| [
{
"created": "Tue, 9 Aug 2016 10:29:39 GMT",
"version": "v1"
}
] | 2016-08-10 | [
[
"Chafik",
"Abderrahim",
""
]
] | MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. They are involved in key biological processes and then may play a major role in the development of human diseases including cancer, in particular their involvement in breast cancer metastasis has been confirmed. Recently, the authors of ref.(\cite{key1} have found that miR-429 may have a role in the inhibition of breast cancer metastasis and have identified its target gene CRKL as a potential candidate. In this paper, by using systems biology tools we have shown that CRKL is involved in positive regulation of ERK1/2 signaling pathway and contribute to the regulation of LYN through a topological generalization of feed forward loop. |
q-bio/0611068 | Andrei Khrennikov | Andrei Khrennikov | Gene expression from polynomial dynamics in the 2-adic information space | Talk: P-adic information space and gene expression. Int. Conference
"Integrative approaches to brain complexity", org.: Grant S., Heintz N.,
Noebels J., Wellcome Trust | null | null | MSI:Vaxjo University, Report 06160, ISSN 1650-2647 | q-bio.OT | null | We perform geometrization of genetics by representing genetic information by
points of the 4-adic {\it information space.} By well known theorem of number
theory this space can also be represented as the 2-adic space. The process of
DNA-reproduction is described by the action of a 4-adic (or equivalently
2-adic) dynamical system. As we know, the genes contain information for
production of proteins. The genetic code is a degenerate map of codons to
proteins. We model this map as functioning of a polynomial dynamical system.
The purely mathematical problem under consideration is to find a dynamical
system reproducing the degenerate structure of the genetic code. We present one
of possible solutions of this problem.
| [
{
"created": "Tue, 21 Nov 2006 17:48:34 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Nov 2006 17:49:47 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Khrennikov",
"Andrei",
""
]
] | We perform geometrization of genetics by representing genetic information by points of the 4-adic {\it information space.} By well known theorem of number theory this space can also be represented as the 2-adic space. The process of DNA-reproduction is described by the action of a 4-adic (or equivalently 2-adic) dynamical system. As we know, the genes contain information for production of proteins. The genetic code is a degenerate map of codons to proteins. We model this map as functioning of a polynomial dynamical system. The purely mathematical problem under consideration is to find a dynamical system reproducing the degenerate structure of the genetic code. We present one of possible solutions of this problem. |
1105.3669 | Sandip Banerjee Dr. | Sandip Banerjee and Ram Keval and S. Gakkhar | Modeling the dynamics of Hepatitis C Virus with combined antiviral drug
therapy: Interferon and Ribavirin | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A mathematical modeling of Hepatitis C Virus (HCV) dynamics has been
presented in this paper. The proposed model, which involves four coupled
ordinary differential equations, describes the interaction of target cells
(hepatocytes), infected cells, infectious virions and non-infectious virions.
The model takes into consideration the addition of ribavirin to interferon
therapy and explains the dynamics regarding biphasic and triphasic decline of
viral load in the model. A critical drug efficiency parameter has been defined
and it is shown that for efficiencies above this critical value, HCV is
eradicated whereas for efficiencies lower this critical value, a new steady
state for infectious virions is reached, which is lower than the previous
steady state.
| [
{
"created": "Wed, 18 May 2011 15:51:18 GMT",
"version": "v1"
}
] | 2011-05-19 | [
[
"Banerjee",
"Sandip",
""
],
[
"Keval",
"Ram",
""
],
[
"Gakkhar",
"S.",
""
]
] | A mathematical modeling of Hepatitis C Virus (HCV) dynamics has been presented in this paper. The proposed model, which involves four coupled ordinary differential equations, describes the interaction of target cells (hepatocytes), infected cells, infectious virions and non-infectious virions. The model takes into consideration the addition of ribavirin to interferon therapy and explains the dynamics regarding biphasic and triphasic decline of viral load in the model. A critical drug efficiency parameter has been defined and it is shown that for efficiencies above this critical value, HCV is eradicated whereas for efficiencies lower this critical value, a new steady state for infectious virions is reached, which is lower than the previous steady state. |
2012.00072 | Jay Lennon | Jay T. Lennon, Frank den Hollander, Maite Wilke-Berenguer, Jochen
Blath | Principles of seed banks: complexity emerging from dormancy | 60 pages, 2 figures, 6 boxes | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Across the tree of life, populations have evolved the capacity to contend
with suboptimal conditions by engaging in dormancy, whereby individuals enter a
reversible state of reduced metabolic activity. The resulting seed banks are
complex, storing information and imparting memory that gives rise to
multi-scale structures and networks spanning collections of cells to entire
ecosystems. We outline the fundamental attributes and emergent phenomena
associated with dormancy and seed banks, with the vision for a unifying and
mathematically based framework that can address problems in the life sciences,
ranging from global change to cancer biology.
| [
{
"created": "Mon, 30 Nov 2020 19:47:24 GMT",
"version": "v1"
},
{
"created": "Thu, 6 May 2021 13:42:02 GMT",
"version": "v2"
}
] | 2021-05-07 | [
[
"Lennon",
"Jay T.",
""
],
[
"Hollander",
"Frank den",
""
],
[
"Wilke-Berenguer",
"Maite",
""
],
[
"Blath",
"Jochen",
""
]
] | Across the tree of life, populations have evolved the capacity to contend with suboptimal conditions by engaging in dormancy, whereby individuals enter a reversible state of reduced metabolic activity. The resulting seed banks are complex, storing information and imparting memory that gives rise to multi-scale structures and networks spanning collections of cells to entire ecosystems. We outline the fundamental attributes and emergent phenomena associated with dormancy and seed banks, with the vision for a unifying and mathematically based framework that can address problems in the life sciences, ranging from global change to cancer biology. |
1302.1904 | Daniel Kaschek | Daniel Kaschek, Jens Timmer | A Unified Approach to Integration and Optimization of Parametric
Ordinary Differential Equations | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parameter estimation in ordinary differential equations, although applied and
refined in various fields of the quantitative sciences, is still confronted
with a variety of difficulties. One major challenge is finding the global
optimum of a log-likelihood function that has several local optima, e.g. in
oscillatory systems. In this publication, we introduce a formulation based on
continuation of the log-likelihood function that allows to restate the
parameter estimation problem as a boundary value problem. By construction, the
ordinary differential equations are solved and the parameters are estimated
both in one step. The formulation as a boundary value problem enables an
optimal transfer of information given by the measurement time courses to the
solution of the estimation problem, thus favoring convergence to the global
optimum. This is demonstrated explicitly for the fully as well as the partially
observed Lotka-Volterra system.
| [
{
"created": "Thu, 7 Feb 2013 22:58:06 GMT",
"version": "v1"
}
] | 2013-02-11 | [
[
"Kaschek",
"Daniel",
""
],
[
"Timmer",
"Jens",
""
]
] | Parameter estimation in ordinary differential equations, although applied and refined in various fields of the quantitative sciences, is still confronted with a variety of difficulties. One major challenge is finding the global optimum of a log-likelihood function that has several local optima, e.g. in oscillatory systems. In this publication, we introduce a formulation based on continuation of the log-likelihood function that allows to restate the parameter estimation problem as a boundary value problem. By construction, the ordinary differential equations are solved and the parameters are estimated both in one step. The formulation as a boundary value problem enables an optimal transfer of information given by the measurement time courses to the solution of the estimation problem, thus favoring convergence to the global optimum. This is demonstrated explicitly for the fully as well as the partially observed Lotka-Volterra system. |
2210.05672 | Haoming Yang | Haoming Yang, Steven Winter, Zhengwu Zhang, David Dunson | Interpretable AI for relating brain structural and functional
connectomes | null | null | null | null | q-bio.NC stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the central problems in neuroscience is understanding how brain
structure relates to function. Naively one can relate the direct connections of
white matter fiber tracts between brain regions of interest (ROIs) to the
increased co-activation in the same pair of ROIs, but the link between
structural and functional connectomes (SCs and FCs) has proven to be much more
complex. To learn a realistic generative model characterizing population
variation in SCs, FCs, and the SC-FC coupling, we develop a graph auto-encoder
that we refer to as Staf-GATE. We trained Staf-GATE with data from the Human
Connectome Project (HCP) and show state-of-the-art performance in predicting FC
and joint generation of SC and FC. In addition, as a crucial component of the
proposed approach, we provide a masking-based algorithm to extract
interpretable inferences about SC-FC coupling. Our interpretation methods
identified important SC subnetworks for FC coupling and relating SC and FC with
sex.
| [
{
"created": "Mon, 10 Oct 2022 18:19:28 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Aug 2023 13:51:32 GMT",
"version": "v2"
}
] | 2023-08-30 | [
[
"Yang",
"Haoming",
""
],
[
"Winter",
"Steven",
""
],
[
"Zhang",
"Zhengwu",
""
],
[
"Dunson",
"David",
""
]
] | One of the central problems in neuroscience is understanding how brain structure relates to function. Naively one can relate the direct connections of white matter fiber tracts between brain regions of interest (ROIs) to the increased co-activation in the same pair of ROIs, but the link between structural and functional connectomes (SCs and FCs) has proven to be much more complex. To learn a realistic generative model characterizing population variation in SCs, FCs, and the SC-FC coupling, we develop a graph auto-encoder that we refer to as Staf-GATE. We trained Staf-GATE with data from the Human Connectome Project (HCP) and show state-of-the-art performance in predicting FC and joint generation of SC and FC. In addition, as a crucial component of the proposed approach, we provide a masking-based algorithm to extract interpretable inferences about SC-FC coupling. Our interpretation methods identified important SC subnetworks for FC coupling and relating SC and FC with sex. |
2010.11861 | Mal\'u Grave | Mal\'u Grave and Alvaro L. G. A. Coutinho | Adaptive Mesh Refinement and Coarsening for Diffusion-Reaction
Epidemiological Models | 33 pages, 31 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The outbreak of COVID-19 in 2020 has led to a surge in the interest in the
mathematical modeling of infectious diseases. Disease transmission may be
modeled as compartmental models, in which the population under study is divided
into compartments and has assumptions about the nature and time rate of
transfer from one compartment to another. Usually, they are composed of a
system of ordinary differential equations (ODE's) in time. A class of such
models considers the Susceptible, Exposed, Infected, Recovered, and Deceased
populations, the SEIRD model. However, these models do not always account for
the movement of individuals from one region to another. In this work, we extend
the formulation of SEIRD compartmental models to diffusion-reaction systems of
partial differential equations to capture the continuous spatio-temporal
dynamics of COVID-19. Since the virus spread is not only through diffusion, we
introduce a source term to the equation system, representing exposed people who
return from travel. We also add the possibility of anisotropic non-homogeneous
diffusion. We implement the whole model in \texttt{libMesh}, an open finite
element library that provides a framework for multiphysics, considering
adaptive mesh refinement and coarsening. Therefore, the model can represent
several spatial scales, adapting the resolution to the disease dynamics. We
verify our model with standard SEIRD models and show several examples
highlighting the present model's new capabilities.
| [
{
"created": "Thu, 22 Oct 2020 16:51:32 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Oct 2020 21:31:01 GMT",
"version": "v2"
}
] | 2020-10-27 | [
[
"Grave",
"Malú",
""
],
[
"Coutinho",
"Alvaro L. G. A.",
""
]
] | The outbreak of COVID-19 in 2020 has led to a surge in the interest in the mathematical modeling of infectious diseases. Disease transmission may be modeled as compartmental models, in which the population under study is divided into compartments and has assumptions about the nature and time rate of transfer from one compartment to another. Usually, they are composed of a system of ordinary differential equations (ODE's) in time. A class of such models considers the Susceptible, Exposed, Infected, Recovered, and Deceased populations, the SEIRD model. However, these models do not always account for the movement of individuals from one region to another. In this work, we extend the formulation of SEIRD compartmental models to diffusion-reaction systems of partial differential equations to capture the continuous spatio-temporal dynamics of COVID-19. Since the virus spread is not only through diffusion, we introduce a source term to the equation system, representing exposed people who return from travel. We also add the possibility of anisotropic non-homogeneous diffusion. We implement the whole model in \texttt{libMesh}, an open finite element library that provides a framework for multiphysics, considering adaptive mesh refinement and coarsening. Therefore, the model can represent several spatial scales, adapting the resolution to the disease dynamics. We verify our model with standard SEIRD models and show several examples highlighting the present model's new capabilities. |
1803.04640 | Jie Hu | Wei Liang, Yuxiao Yang, Yusi Fang, Zhongying Zhao, Jie Hu | Bayesian Detection of Abnormal ADS in Mutant Caenorhabditis elegans
Embryos | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell division timing is critical for cell fate specification and
morphogenesis during embryogenesis. How division timings are regulated among
cells during development is poorly understood. Here we focus on the comparison
of asynchrony of division between sister cells (ADS) between wild-type and
mutant individuals of Caenorhabditis elegans. Since the replicate number of
mutant individuals of each mutated gene, usually one, is far smaller than that
of wild-type, direct comparison of two distributions of ADS between wild-type
and mutant type, such as Kolmogorov- Smirnov test, is not feasible. On the
other hand, we find that sometimes ADS is correlated with the life span of
corresponding mother cell in wild-type. Hence, we apply a semiparametric
Bayesian quantile regression method to estimate the 95% confidence interval
curve of ADS with respect to life span of mother cell of wild-type individuals.
Then, mutant-type ADSs outside the corresponding confidence interval are
selected out as abnormal one with a significance level of 0.05. Simulation
study demonstrates the accuracy of our method and Gene Enrichment Analysis
validates the results of real data sets.
| [
{
"created": "Tue, 13 Mar 2018 06:08:19 GMT",
"version": "v1"
}
] | 2018-03-14 | [
[
"Liang",
"Wei",
""
],
[
"Yang",
"Yuxiao",
""
],
[
"Fang",
"Yusi",
""
],
[
"Zhao",
"Zhongying",
""
],
[
"Hu",
"Jie",
""
]
] | Cell division timing is critical for cell fate specification and morphogenesis during embryogenesis. How division timings are regulated among cells during development is poorly understood. Here we focus on the comparison of asynchrony of division between sister cells (ADS) between wild-type and mutant individuals of Caenorhabditis elegans. Since the replicate number of mutant individuals of each mutated gene, usually one, is far smaller than that of wild-type, direct comparison of two distributions of ADS between wild-type and mutant type, such as Kolmogorov- Smirnov test, is not feasible. On the other hand, we find that sometimes ADS is correlated with the life span of corresponding mother cell in wild-type. Hence, we apply a semiparametric Bayesian quantile regression method to estimate the 95% confidence interval curve of ADS with respect to life span of mother cell of wild-type individuals. Then, mutant-type ADSs outside the corresponding confidence interval are selected out as abnormal one with a significance level of 0.05. Simulation study demonstrates the accuracy of our method and Gene Enrichment Analysis validates the results of real data sets. |
2103.10795 | Pablo M. Gleiser | Andr\'es H. Calder\'on, Romina A. Capellino, Dami\'an Dellavale, D.
Lorena Franco, Pablo M. Gleiser, Sergio Lindenbaum, Mara L\'opez-Wortzman,
Sabrina C. Riva, Fernanda R. Rom\'an, and Sebasti\'an Risau-Gusman | Sleep quality, chronotype and social jet lag of adolescents from a
population with a very late chronotype | 19 pages, 4 tables, 10 figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Sleep disorders can be a negative factor both for learning as for the mental
and physical development of adolescents. It has been shown that, in many
populations, adolescents tend to have a poor sleep quality, and a very late
chronotype. Furthermore, these features peak at adolescence, in the sense that
adults tend to sleep better and have an earlier chronotype. But what happens
when we consider adolescents in a population where already adults have poor
sleep quality and a very late chronotype? We have conducted two non-clinical
studies in the city of Bariloche, Argentina aimed at measuring sleep quality,
chronotype, and social jet lag, using the Pittsburgh and Munich questionnaires.
These were administered individually to groups of high school students, as well
as to smaller samples of adults and preadolescents, in order to study
differences between adolescents and these groups. The results show that in this
population sleep quality is much poorer than in most other healthy populations
recorded elsewhere. Furthermore, sleep quality is consistently worse for
adolescents than for the other groups. The difference with adults seems to be
due mainly to increased daytime sleepiness and sleep latency, whereas the
difference with preadolescents seems to be due mainly to shorter sleep
duration. We also found that the chronotypes of all the groups are very late,
with a peak at an age between 18 and 24 ys. Social jet lag and sleep onset
latency are also large, and they peak at adolescence, which suggests that they
might be closely related to the large prevalence of poor sleep quality that we
find in adolescents.
| [
{
"created": "Fri, 19 Mar 2021 13:32:00 GMT",
"version": "v1"
}
] | 2021-03-22 | [
[
"Calderón",
"Andrés H.",
""
],
[
"Capellino",
"Romina A.",
""
],
[
"Dellavale",
"Damián",
""
],
[
"Franco",
"D. Lorena",
""
],
[
"Gleiser",
"Pablo M.",
""
],
[
"Lindenbaum",
"Sergio",
""
],
[
"López-Wortzman",
"Mara",
""
],
[
"Riva",
"Sabrina C.",
""
],
[
"Román",
"Fernanda R.",
""
],
[
"Risau-Gusman",
"Sebastián",
""
]
] | Sleep disorders can be a negative factor both for learning as for the mental and physical development of adolescents. It has been shown that, in many populations, adolescents tend to have a poor sleep quality, and a very late chronotype. Furthermore, these features peak at adolescence, in the sense that adults tend to sleep better and have an earlier chronotype. But what happens when we consider adolescents in a population where already adults have poor sleep quality and a very late chronotype? We have conducted two non-clinical studies in the city of Bariloche, Argentina aimed at measuring sleep quality, chronotype, and social jet lag, using the Pittsburgh and Munich questionnaires. These were administered individually to groups of high school students, as well as to smaller samples of adults and preadolescents, in order to study differences between adolescents and these groups. The results show that in this population sleep quality is much poorer than in most other healthy populations recorded elsewhere. Furthermore, sleep quality is consistently worse for adolescents than for the other groups. The difference with adults seems to be due mainly to increased daytime sleepiness and sleep latency, whereas the difference with preadolescents seems to be due mainly to shorter sleep duration. We also found that the chronotypes of all the groups are very late, with a peak at an age between 18 and 24 ys. Social jet lag and sleep onset latency are also large, and they peak at adolescence, which suggests that they might be closely related to the large prevalence of poor sleep quality that we find in adolescents. |
1403.3256 | Wan-Chung Hu Dr. | Wan-Chung Hu | Parkinson disease is a TH17 dominant autoimmune disorder against
accumulated alpha-synuclein | null | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parkinson disease is a very common neurodegenerative disorder. Patients
usually undergo destruction of substantia nigra to develop typical symptoms
such as resting tremor, hypokinesia, and rigidity. However, the exact mechanism
of Parkinson disease is still unknown, so it is called idiopathic Parkinsonism.
According to my microarray analysis of peripheral blood leukocytes and
substantia nigra brain tissue, I propose that Parkinson disease is actually a
TH17 dominant autoimmune disease. Based on the microarray data in substantia
nigra, HSP40, HSP70, HSP90, HSP27, HSP105, TLR5, TLR7, CEBPB, CEBPG, FOS, and
caspase1 are significantly up-regulated. In peripheral leukocytes, NFKB1A,
CEBPD, FOS, retinoic receptor alpha, suppressor of IKK epsilon, S100A11, G-CSF,
MMP9, IL-1 receptor, IL-8 receptor, TNF receptor, caspase8, c1q receptor,
cathepsin Z, HLA-G, complement receptor1, and complement 5a receptor. General
immune related genes are also up-regulated including ILF2, CD22, CD3E, BLNK,
ILF3, TCR alpha, TCR zeta, TCR delta, LAT, ITK, Ly9, and BANK1. The autoantigen
is mainly alpha-synuclein. After knowing the exact disease pathophysiology, we
can develop better drugs to prevent or control the detrimental disorder.
| [
{
"created": "Thu, 21 Nov 2013 07:31:13 GMT",
"version": "v1"
}
] | 2014-03-14 | [
[
"Hu",
"Wan-Chung",
""
]
] | Parkinson disease is a very common neurodegenerative disorder. Patients usually undergo destruction of substantia nigra to develop typical symptoms such as resting tremor, hypokinesia, and rigidity. However, the exact mechanism of Parkinson disease is still unknown, so it is called idiopathic Parkinsonism. According to my microarray analysis of peripheral blood leukocytes and substantia nigra brain tissue, I propose that Parkinson disease is actually a TH17 dominant autoimmune disease. Based on the microarray data in substantia nigra, HSP40, HSP70, HSP90, HSP27, HSP105, TLR5, TLR7, CEBPB, CEBPG, FOS, and caspase1 are significantly up-regulated. In peripheral leukocytes, NFKB1A, CEBPD, FOS, retinoic receptor alpha, suppressor of IKK epsilon, S100A11, G-CSF, MMP9, IL-1 receptor, IL-8 receptor, TNF receptor, caspase8, c1q receptor, cathepsin Z, HLA-G, complement receptor1, and complement 5a receptor. General immune related genes are also up-regulated including ILF2, CD22, CD3E, BLNK, ILF3, TCR alpha, TCR zeta, TCR delta, LAT, ITK, Ly9, and BANK1. The autoantigen is mainly alpha-synuclein. After knowing the exact disease pathophysiology, we can develop better drugs to prevent or control the detrimental disorder. |
1907.10879 | Pau Vilimelis Aceituno | Pau Vilimelis Aceituno and Masud Ehsani and J\"urgen Jost | Synaptic Time-Dependent Plasticity Leads to Efficient Coding of
Predictions | 27 Pages, 5 Figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Latency reduction of postsynaptic spikes is a well-known effect of Synaptic
Time-Dependent Plasticity. We expand this notion for long postsynaptic spike
trains, showing that, for a fixed input spike train, STDP reduces the number of
postsynaptic spikes and concentrates the remaining ones. Then we study the
consequences of this phenomena in terms of coding, finding that this mechanism
improves the neural code by increasing the signal-to-noise ratio and lowering
the metabolic costs of frequent stimuli. Finally, we illustrate that the
reduction of postsynaptic latencies can lead to the emergence of predictions.
| [
{
"created": "Thu, 25 Jul 2019 07:46:22 GMT",
"version": "v1"
}
] | 2019-07-26 | [
[
"Aceituno",
"Pau Vilimelis",
""
],
[
"Ehsani",
"Masud",
""
],
[
"Jost",
"Jürgen",
""
]
] | Latency reduction of postsynaptic spikes is a well-known effect of Synaptic Time-Dependent Plasticity. We expand this notion for long postsynaptic spike trains, showing that, for a fixed input spike train, STDP reduces the number of postsynaptic spikes and concentrates the remaining ones. Then we study the consequences of this phenomena in terms of coding, finding that this mechanism improves the neural code by increasing the signal-to-noise ratio and lowering the metabolic costs of frequent stimuli. Finally, we illustrate that the reduction of postsynaptic latencies can lead to the emergence of predictions. |
2307.12176 | Ruben Meuwese | Steven Kelk, Ruben Meuwese | Agreement forests of caterpillar trees: complexity, kernelization and
branching | 33 pages, 15 figures | null | null | null | q-bio.PE cs.DS math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a set $X$ of species, a phylogenetic tree is an unrooted binary tree
whose leaves are bijectively labelled by $X$. Such trees can be used to show
the way species evolve over time. One way of understanding how topologically
different two phylogenetic trees are, is to construct a minimum-size agreement
forest: a partition of $X$ into the smallest number of blocks, such that the
blocks induce homeomorphic, non-overlapping subtrees in both trees. This
comparison yields insight into commonalities and differences in the evolution
of $X$ across the two trees. Computing a smallest agreement forest is NP-hard
(Hein, Jiang, Wang and Zhang, Discrete Applied Mathematics 71(1-3), 1996). In
this work we study the problem on caterpillars, which are path-like
phylogenetic trees. We will demonstrate that, even if we restrict the input to
this highly restricted subclass, the problem remains NP-hard and is in fact
APX-hard. Furthermore we show that for caterpillars two standard reductions
rules well known in the literature yield a tight kernel of size at most $7k$,
compared to $15k$ for general trees (Kelk and Simone, SIAM Journal on Discrete
Mathematics 33(3), 2019). Finally we demonstrate that we can determine if two
caterpillars have an agreement forest with at most $k$ blocks in $O^*(2.49^k)$
time, compared to $O^*(3^k)$ for general trees (Chen, Fan and Sze, Theoretical
Computater Science 562, 2015), where $O^*(.)$ suppresses polynomial factors.
| [
{
"created": "Sat, 22 Jul 2023 22:10:24 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Aug 2023 12:10:51 GMT",
"version": "v2"
}
] | 2023-09-01 | [
[
"Kelk",
"Steven",
""
],
[
"Meuwese",
"Ruben",
""
]
] | Given a set $X$ of species, a phylogenetic tree is an unrooted binary tree whose leaves are bijectively labelled by $X$. Such trees can be used to show the way species evolve over time. One way of understanding how topologically different two phylogenetic trees are, is to construct a minimum-size agreement forest: a partition of $X$ into the smallest number of blocks, such that the blocks induce homeomorphic, non-overlapping subtrees in both trees. This comparison yields insight into commonalities and differences in the evolution of $X$ across the two trees. Computing a smallest agreement forest is NP-hard (Hein, Jiang, Wang and Zhang, Discrete Applied Mathematics 71(1-3), 1996). In this work we study the problem on caterpillars, which are path-like phylogenetic trees. We will demonstrate that, even if we restrict the input to this highly restricted subclass, the problem remains NP-hard and is in fact APX-hard. Furthermore we show that for caterpillars two standard reductions rules well known in the literature yield a tight kernel of size at most $7k$, compared to $15k$ for general trees (Kelk and Simone, SIAM Journal on Discrete Mathematics 33(3), 2019). Finally we demonstrate that we can determine if two caterpillars have an agreement forest with at most $k$ blocks in $O^*(2.49^k)$ time, compared to $O^*(3^k)$ for general trees (Chen, Fan and Sze, Theoretical Computater Science 562, 2015), where $O^*(.)$ suppresses polynomial factors. |
1511.01429 | Kevin Emmett | Kevin Emmett, Raul Rabadan | Quantifying Reticulation in Phylogenetic Complexes Using Homology | 4 pages, 8 figures. Accepted for presentation at BICT 2015 Special
Track on Topology-driven bio-inspired methods and models for complex systems
(TOPDRIM4bio) | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reticulate evolutionary processes result in phylogenetic histories that
cannot be modeled using a tree topology. Here, we apply methods from
topological data analysis to molecular sequence data with reticulations. Using
a simple example, we demonstrate the correspondence between nontrivial higher
homology and reticulate evolution. We discuss the sensitivity of the standard
filtration and show cases where reticulate evolution can fail to be detected.
We introduce an extension of the standard framework and define the median
complex as a construction to recover signal of the frequency and scale of
reticulate evolution by inferring and imputing putative ancestral states.
Finally, we apply our methods to two datasets from phylogenetics. Our work
expands on earlier ideas of using topology to extract important evolutionary
features from genomic data.
| [
{
"created": "Wed, 4 Nov 2015 18:44:15 GMT",
"version": "v1"
}
] | 2015-11-05 | [
[
"Emmett",
"Kevin",
""
],
[
"Rabadan",
"Raul",
""
]
] | Reticulate evolutionary processes result in phylogenetic histories that cannot be modeled using a tree topology. Here, we apply methods from topological data analysis to molecular sequence data with reticulations. Using a simple example, we demonstrate the correspondence between nontrivial higher homology and reticulate evolution. We discuss the sensitivity of the standard filtration and show cases where reticulate evolution can fail to be detected. We introduce an extension of the standard framework and define the median complex as a construction to recover signal of the frequency and scale of reticulate evolution by inferring and imputing putative ancestral states. Finally, we apply our methods to two datasets from phylogenetics. Our work expands on earlier ideas of using topology to extract important evolutionary features from genomic data. |
1511.09379 | Gennaro Vitucci | Gennaro Vitucci, Ivan Argatov and Gennady Mishuris | An asymptotic model for the deformation of a transversely isotropic,
transversely homogeneous biphasic cartilage layer | null | null | null | null | q-bio.TO cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the present paper, an asymptotic model is constructed for the short-time
deformation of an articular cartilage layer modeled as transversely isotropic,
transversely homogeneous (TITH) biphasic material. It is assumed that the layer
thickness is relatively small compared with the characteristic size of the
normal surface load applied to the upper surface of the cartilage layer, while
the bottom surface is assumed to be firmly attached to a rigid impermeable
substrate. In view of applications to articular contact problems it is assumed
that the interstitial fluid is not allowed to escape through the articular
surface.
| [
{
"created": "Mon, 9 Nov 2015 22:18:01 GMT",
"version": "v1"
}
] | 2015-12-01 | [
[
"Vitucci",
"Gennaro",
""
],
[
"Argatov",
"Ivan",
""
],
[
"Mishuris",
"Gennady",
""
]
] | In the present paper, an asymptotic model is constructed for the short-time deformation of an articular cartilage layer modeled as transversely isotropic, transversely homogeneous (TITH) biphasic material. It is assumed that the layer thickness is relatively small compared with the characteristic size of the normal surface load applied to the upper surface of the cartilage layer, while the bottom surface is assumed to be firmly attached to a rigid impermeable substrate. In view of applications to articular contact problems it is assumed that the interstitial fluid is not allowed to escape through the articular surface. |
1709.03408 | Victor M. Eguiluz | A Traveset, C Tur, VM Egu\'iluz | Plant survival and keystone pollinator species in stochastic
coextinction models: role of intrinsic dependence on animal-pollination | (37 pages, 9 figures, including supplementary information) | Scientific Reports 7, 6915 (2017) | 10.1038/s41598-017-07037-7 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Coextinction models are useful to understand community robustness to species
loss and resilience to disturbances. We simulated pollinator extinctions in
pollination networks by using a hybrid model that combined a recently developed
stochastic coextinction model (SCM) for plant extinctions and a topological
model (TCM) for animal extinctions. Our model accounted for variation in
interaction strengths and included empirical estimates of plant dependence on
pollinators to set seeds. The stochastic nature of such model allowed us
determining plant survival to single (and multiple) extinction events, and
identifying which pollinators (keystone species) were more likely to trigger
secondary extinctions. Consistently across three different pollinator removal
sequences, plant robustness was lower than in a pure TCM, and plant survival
was more determined by dependence on the mutualism than by interaction
strength. As expected, highly connected and dependent plants were the most
sensitive to pollinator loss and collapsed faster in extinction cascades. We
predict that the relationship between dependence and plant connectivity is
crucial to determine network robustness to interaction loss. Finally, we showed
that honeybees and several beetles were keystone species in our communities.
This information is of great value to foresee consequences of pollinator losses
facing current global change and to identify target species for effective
conservation.
| [
{
"created": "Mon, 11 Sep 2017 14:32:54 GMT",
"version": "v1"
}
] | 2017-09-12 | [
[
"Traveset",
"A",
""
],
[
"Tur",
"C",
""
],
[
"Eguíluz",
"VM",
""
]
] | Coextinction models are useful to understand community robustness to species loss and resilience to disturbances. We simulated pollinator extinctions in pollination networks by using a hybrid model that combined a recently developed stochastic coextinction model (SCM) for plant extinctions and a topological model (TCM) for animal extinctions. Our model accounted for variation in interaction strengths and included empirical estimates of plant dependence on pollinators to set seeds. The stochastic nature of such model allowed us determining plant survival to single (and multiple) extinction events, and identifying which pollinators (keystone species) were more likely to trigger secondary extinctions. Consistently across three different pollinator removal sequences, plant robustness was lower than in a pure TCM, and plant survival was more determined by dependence on the mutualism than by interaction strength. As expected, highly connected and dependent plants were the most sensitive to pollinator loss and collapsed faster in extinction cascades. We predict that the relationship between dependence and plant connectivity is crucial to determine network robustness to interaction loss. Finally, we showed that honeybees and several beetles were keystone species in our communities. This information is of great value to foresee consequences of pollinator losses facing current global change and to identify target species for effective conservation. |
1910.08000 | Reyhane Alidousti | Reyhane Alidousti, Mostafa Shakhsi-Niaee | Recent applicable delivery approaches of peptide nucleic acids to the
target cells | 6 pages , The 6th International Congress on Development and Promotion
of Fundamental Science and Technolpgy in Society , Tehran , Iran | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Peptide nucleic acids (PNAs) are artificial nucleic acids with a peptide
backbone instead of sugar phosphate backbone of DNA or RNA. Their resistance to
degradation, selectivity and greater binding affinity in comparison to usual
nucleic acids led to consideration of their great potential for different
applications. For example, they can be used in molecular diagnostics and
antisense therapeutics such as antimicrobial agents or gene regulatory tools.
On the other hand, large hydrophilic property of PNA molecules, which inhibit
them to cross cell membranes readily, is an obstacle for their delivery to
considered target cells and a limiting criterion for their applications.
Therefore, PNA delivery technologies have been developing to hurdle this
limitation. For example, addition of lysine residues, charged membrane
penetrating peptide sequences, PNAs conjugated with antibodies or steroids,
cationic liposomes as carriers of PNA conjugates, protective peptides and
technology of photochemical internalization (PCI) as well as by the recent
technology of nanoparticle-based delivery have been employed. In this article
we compared different delivery technologies which can be applicable to PNAs. As
a result nanoparticle-based delivery showed more advantages in comparison to
others and its application is growing fast. Keywords: Target cell delivery,
Peptide nucleic acid, PNA, Delivery.
| [
{
"created": "Tue, 24 Sep 2019 19:23:47 GMT",
"version": "v1"
}
] | 2019-10-18 | [
[
"Alidousti",
"Reyhane",
""
],
[
"Shakhsi-Niaee",
"Mostafa",
""
]
] | Peptide nucleic acids (PNAs) are artificial nucleic acids with a peptide backbone instead of sugar phosphate backbone of DNA or RNA. Their resistance to degradation, selectivity and greater binding affinity in comparison to usual nucleic acids led to consideration of their great potential for different applications. For example, they can be used in molecular diagnostics and antisense therapeutics such as antimicrobial agents or gene regulatory tools. On the other hand, large hydrophilic property of PNA molecules, which inhibit them to cross cell membranes readily, is an obstacle for their delivery to considered target cells and a limiting criterion for their applications. Therefore, PNA delivery technologies have been developing to hurdle this limitation. For example, addition of lysine residues, charged membrane penetrating peptide sequences, PNAs conjugated with antibodies or steroids, cationic liposomes as carriers of PNA conjugates, protective peptides and technology of photochemical internalization (PCI) as well as by the recent technology of nanoparticle-based delivery have been employed. In this article we compared different delivery technologies which can be applicable to PNAs. As a result nanoparticle-based delivery showed more advantages in comparison to others and its application is growing fast. Keywords: Target cell delivery, Peptide nucleic acid, PNA, Delivery. |
2202.12710 | Evgenii Vityaev | Evgenii Vityaev, Anton Kolonin, Andrey Kurpatov, Artem Molchanov | Brain Principles Programming | to be submitted to the AGI-22 | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the monograph, STRONG ARTIFICIAL INTELLIGENCE. On the Approaches to
Superintelligence, published by Sberbank, provides a cross-disciplinary review
of general artificial intelligence. As an anthropomorphic direction of
research, it considers Brain Principles Programming, BPP) the formalization of
universal mechanisms (principles) of the brain's work with information, which
are implemented at all levels of the organization of nervous tissue. This
monograph provides a formalization of these principles in terms of the category
theory. However, this formalization is not enough to develop algorithms for
working with information. In this paper, for the description and modeling of
Brain Principles Programming, it is proposed to apply mathematical models and
algorithms developed by us earlier that model cognitive functions, which are
based on well-known physiological, psychological and other natural science
theories. The paper uses mathematical models and algorithms of the following
theories: P.K.Anokhin's Theory of Functional Brain Systems, Eleonor Rosh's
prototypical categorization theory, Bob Rehter's theory of causal models and
natural classification. As a result, the formalization of the BPP is obtained
and computer examples are given that demonstrate the algorithm's operation.
| [
{
"created": "Sun, 13 Feb 2022 13:41:44 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Mar 2022 12:53:37 GMT",
"version": "v2"
},
{
"created": "Sun, 3 Apr 2022 10:11:13 GMT",
"version": "v3"
}
] | 2022-04-05 | [
[
"Vityaev",
"Evgenii",
""
],
[
"Kolonin",
"Anton",
""
],
[
"Kurpatov",
"Andrey",
""
],
[
"Molchanov",
"Artem",
""
]
] | In the monograph, STRONG ARTIFICIAL INTELLIGENCE. On the Approaches to Superintelligence, published by Sberbank, provides a cross-disciplinary review of general artificial intelligence. As an anthropomorphic direction of research, it considers Brain Principles Programming, BPP) the formalization of universal mechanisms (principles) of the brain's work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of Brain Principles Programming, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin's Theory of Functional Brain Systems, Eleonor Rosh's prototypical categorization theory, Bob Rehter's theory of causal models and natural classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithm's operation. |
1704.04268 | John Rhodes | Elizabeth S. Allman and James H. Degnan and John A. Rhodes | Split probabilities and species tree inference under the multispecies
coalescent model | 43 pages | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using topological summaries of gene trees as a basis for species tree
inference is a promising approach to obtain acceptable speed on genomic-scale
datasets, and to avoid some undesirable modeling assumptions. Here we study the
probabilities of splits on gene trees under the multispecies coalescent model,
and how their features might inform species tree inference. After investigating
the behavior of split consensus methods, we investigate split invariants ---
that is, polynomial relationships between split probabilities. These invariants
are then used to show that, even though a split is an unrooted notion, split
probabilities retain enough information to identify the rooted species tree
topology for trees of more than 5 taxa, with one possible 6-taxon exception.
| [
{
"created": "Thu, 13 Apr 2017 19:45:39 GMT",
"version": "v1"
}
] | 2017-04-17 | [
[
"Allman",
"Elizabeth S.",
""
],
[
"Degnan",
"James H.",
""
],
[
"Rhodes",
"John A.",
""
]
] | Using topological summaries of gene trees as a basis for species tree inference is a promising approach to obtain acceptable speed on genomic-scale datasets, and to avoid some undesirable modeling assumptions. Here we study the probabilities of splits on gene trees under the multispecies coalescent model, and how their features might inform species tree inference. After investigating the behavior of split consensus methods, we investigate split invariants --- that is, polynomial relationships between split probabilities. These invariants are then used to show that, even though a split is an unrooted notion, split probabilities retain enough information to identify the rooted species tree topology for trees of more than 5 taxa, with one possible 6-taxon exception. |
2010.07888 | Anna Levina | Roxana Zeraati, Viola Priesemann, Anna Levina | Self-organization toward criticality by synaptic plasticity | 34 pages, 7 figures | null | 10.3389/fphy.2021.619661 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Self-organized criticality has been proposed to be a universal mechanism for
the emergence of scale-free dynamics in many complex systems, and possibly in
the brain. While such scale-free patterns were identified experimentally in
many different types of neural recordings, the biological principles behind
their emergence remained unknown. Utilizing different network models and
motivated by experimental observations, synaptic plasticity was proposed as a
possible mechanism to self-organize brain dynamics towards a critical point. In
this review, we discuss how various biologically plausible plasticity rules
operating across multiple timescales are implemented in the models and how they
alter the network's dynamical state through modification of number and strength
of the connections between the neurons. Some of these rules help to stabilize
criticality, some need additional mechanisms to prevent divergence from the
critical state. We propose that rules that are capable of bringing the network
to criticality can be classified by how long the near-critical dynamics
persists after their disabling. Finally, we discuss the role of
self-organization and criticality in computation. Overall, the concept of
criticality helps to shed light on brain function and self-organization, yet
the overall dynamics of living neural networks seem to harnesses not only
criticality for computation, but also deviations thereof.
| [
{
"created": "Thu, 15 Oct 2020 17:08:05 GMT",
"version": "v1"
}
] | 2021-05-11 | [
[
"Zeraati",
"Roxana",
""
],
[
"Priesemann",
"Viola",
""
],
[
"Levina",
"Anna",
""
]
] | Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics towards a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network's dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof. |
1509.05556 | Hengtong Wang | Hengtong Wang and Yong Chen | Firing dynamics of an autaptic neuron | null | null | 10.1088/1674-1056/24/12/128709 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autapses are synapses that connect a neuron to itself in the nervous system.
Previously, both experimental and theoretical studies have demonstrated that
autaptic connections in the nervous system have a significant physiological
function. Autapses in nature provide self-delayed feedback, thus introducing an
additional time scale to neuronal activities and causing many dynamic behaviors
in neurons. Recently, theoretical studies have revealed that an autapse
provides a control option for adjusting the response of a neuron: e.g., an
autaptic connection can cause the electrical activities of the Hindmarsh-Rose
neuron to switch between quiescent, periodic, and chaotic firing patterns; an
autapse can enhance or suppress the mode-locking status of a neuron injected
with sinusoidal current; and the firing frequency and interspike interval
distributions of the response spike train can also be modified by the autapse.
In this paper, we review recent studies that showed how an autapse affects the
response of a single neuron.
| [
{
"created": "Fri, 18 Sep 2015 09:16:12 GMT",
"version": "v1"
}
] | 2016-01-11 | [
[
"Wang",
"Hengtong",
""
],
[
"Chen",
"Yong",
""
]
] | Autapses are synapses that connect a neuron to itself in the nervous system. Previously, both experimental and theoretical studies have demonstrated that autaptic connections in the nervous system have a significant physiological function. Autapses in nature provide self-delayed feedback, thus introducing an additional time scale to neuronal activities and causing many dynamic behaviors in neurons. Recently, theoretical studies have revealed that an autapse provides a control option for adjusting the response of a neuron: e.g., an autaptic connection can cause the electrical activities of the Hindmarsh-Rose neuron to switch between quiescent, periodic, and chaotic firing patterns; an autapse can enhance or suppress the mode-locking status of a neuron injected with sinusoidal current; and the firing frequency and interspike interval distributions of the response spike train can also be modified by the autapse. In this paper, we review recent studies that showed how an autapse affects the response of a single neuron. |
1710.06017 | Davide Maspero | Davide Maspero, Claudio Isella, Marzia Di Filippo, Alex Graudenzi,
Sara Erika Bellomo, Marco Antoniotti, Giancarlo Mauri, Enzo Medico, Chiara
Damiani | Metabolic enrichment through functional gene rules | Preprint of proceedings of CIBB 2017 | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | It is well known that tumors originating from the same tissue have different
prognosis and sensitivity to treatments. Over the last decade, cancer genomics
consortia like the Cancer Genome Atlas (TCGA) have been generating thousands of
cross-sectional data, for thousands of human primary tumors originated from
various tissues. Thanks to that public database, it is today possible to
analyze a broad range of relevant information such as gene sequences,
expression profiles or metabolite footprints, to capture tumor molecular
heterogeneity and improve patient stratification and clinical management. To
this aim, it is common practice to analyze datasets grouped into clusters based
on clinical observations and/or molecular features. However, the identification
of specific properties of each cluster that may be effectively targeted by
therapeutic drugs still represents a challenging task. We define a method to
generate an activity score for the metabolic reactions of different clusters of
patients based on their transcriptional profile. This approach reduces the
number of variables from many genes to few reactions, by aggregating
transcriptional information associated to the same enzymatic reaction according
to gene-enzyme and enzyme-reaction rules. We also applied the methodology to a
dataset of 244 RNAseq transcriptional profiles taken from patients with
colorectal cancer (CRC). CRC samples are typically divided into two sub-types:
(i) tumors with microsatellite instability (MSI), associated with
hyper-mutation and with CpG island methylation phenotype, and (ii)
microsatellite stable (MSS) tumors, typically endowed with chromosomal
instability. We report some key differences in the central carbon metabolism of
the two clusters. We also show how the method can be used to describe the
metabolism of individual patients and cluster them exclusively based on
metabolic features.
| [
{
"created": "Mon, 16 Oct 2017 22:36:54 GMT",
"version": "v1"
}
] | 2017-10-18 | [
[
"Maspero",
"Davide",
""
],
[
"Isella",
"Claudio",
""
],
[
"Di Filippo",
"Marzia",
""
],
[
"Graudenzi",
"Alex",
""
],
[
"Bellomo",
"Sara Erika",
""
],
[
"Antoniotti",
"Marco",
""
],
[
"Mauri",
"Giancarlo",
""
],
[
"Medico",
"Enzo",
""
],
[
"Damiani",
"Chiara",
""
]
] | It is well known that tumors originating from the same tissue have different prognosis and sensitivity to treatments. Over the last decade, cancer genomics consortia like the Cancer Genome Atlas (TCGA) have been generating thousands of cross-sectional data, for thousands of human primary tumors originated from various tissues. Thanks to that public database, it is today possible to analyze a broad range of relevant information such as gene sequences, expression profiles or metabolite footprints, to capture tumor molecular heterogeneity and improve patient stratification and clinical management. To this aim, it is common practice to analyze datasets grouped into clusters based on clinical observations and/or molecular features. However, the identification of specific properties of each cluster that may be effectively targeted by therapeutic drugs still represents a challenging task. We define a method to generate an activity score for the metabolic reactions of different clusters of patients based on their transcriptional profile. This approach reduces the number of variables from many genes to few reactions, by aggregating transcriptional information associated to the same enzymatic reaction according to gene-enzyme and enzyme-reaction rules. We also applied the methodology to a dataset of 244 RNAseq transcriptional profiles taken from patients with colorectal cancer (CRC). CRC samples are typically divided into two sub-types: (i) tumors with microsatellite instability (MSI), associated with hyper-mutation and with CpG island methylation phenotype, and (ii) microsatellite stable (MSS) tumors, typically endowed with chromosomal instability. We report some key differences in the central carbon metabolism of the two clusters. We also show how the method can be used to describe the metabolism of individual patients and cluster them exclusively based on metabolic features. |
1612.06719 | Tom De Smedt | Tom De Smedt, Lieven Menschaert, Pieter Heremans, Ludivine Lechat,
Ga\"elle Dhooghe | An EEG study of creativity in expert classical musicians | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Previous research has shown positive correlations between EEG alpha activity
and performing creative tasks. In this study, expert classical musicians (n=4)
were asked to play their instrument while being monitored with a wireless EEG
headset. Data was collected during two rehearsal types: (a) in their regular,
fixed ensemble;; (b) in an improvised, mixed ensemble with unfamiliar musicians
and less rehearsal time. A positive correlation was found between alpha power
and the improvised setup (p<0.01, d=0.4). A positive correlation was also found
between alpha power and more intense play (p<0.01, d=0.2). There was a negative
correlation between alpha power and arousal due to stress, e.g., frowning after
playing a false note (p<0.01, d=0.6). Finally, the real-time capabilities of
wireless EEG monitoring were explored with a data visualisation during live
performance on stage.
| [
{
"created": "Sun, 18 Dec 2016 22:45:04 GMT",
"version": "v1"
}
] | 2016-12-21 | [
[
"De Smedt",
"Tom",
""
],
[
"Menschaert",
"Lieven",
""
],
[
"Heremans",
"Pieter",
""
],
[
"Lechat",
"Ludivine",
""
],
[
"Dhooghe",
"Gaëlle",
""
]
] | Previous research has shown positive correlations between EEG alpha activity and performing creative tasks. In this study, expert classical musicians (n=4) were asked to play their instrument while being monitored with a wireless EEG headset. Data was collected during two rehearsal types: (a) in their regular, fixed ensemble;; (b) in an improvised, mixed ensemble with unfamiliar musicians and less rehearsal time. A positive correlation was found between alpha power and the improvised setup (p<0.01, d=0.4). A positive correlation was also found between alpha power and more intense play (p<0.01, d=0.2). There was a negative correlation between alpha power and arousal due to stress, e.g., frowning after playing a false note (p<0.01, d=0.6). Finally, the real-time capabilities of wireless EEG monitoring were explored with a data visualisation during live performance on stage. |
2112.10951 | Misha Perepelitsa | Misha Perepelitsa | General-purpose cooperativeness and altruism in humans: elements of the
mathematical framework for the Interdependence Hypothesis | 13 pages, 3 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We propose a decision-making model for joint intentionality by interpreting
it as group-mindedness at the microlevel. We apply this model to give a formal
justification of the first part of the Interdependence Hypothesis due to
Tomasello et al. [Current Anthropology, 2012] which asserts that the emergence
of joint intentionality evolved due to the challenges of difficult
collaborative foraging practices among early humans, and that its evolution led
to robust collaboration and some form of altruism.
In another application of the microlevel group-mindedness we consider the
problem of establishing cooperation in high-risk-of-defection strategic
conflicts and we show that the emergence of cooperation in such situations can
be explained in the context of cultural group selection as the result of
adaptive learning.
| [
{
"created": "Tue, 21 Dec 2021 02:52:36 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Dec 2021 01:41:55 GMT",
"version": "v2"
},
{
"created": "Sat, 22 Jan 2022 01:00:33 GMT",
"version": "v3"
},
{
"created": "Tue, 25 Jan 2022 02:05:41 GMT",
"version": "v4"
}
] | 2022-01-26 | [
[
"Perepelitsa",
"Misha",
""
]
] | We propose a decision-making model for joint intentionality by interpreting it as group-mindedness at the microlevel. We apply this model to give a formal justification of the first part of the Interdependence Hypothesis due to Tomasello et al. [Current Anthropology, 2012] which asserts that the emergence of joint intentionality evolved due to the challenges of difficult collaborative foraging practices among early humans, and that its evolution led to robust collaboration and some form of altruism. In another application of the microlevel group-mindedness we consider the problem of establishing cooperation in high-risk-of-defection strategic conflicts and we show that the emergence of cooperation in such situations can be explained in the context of cultural group selection as the result of adaptive learning. |
1507.06160 | Dmitry Zubarev | Dmitry Yu. Zubarev and Leonardo A. Pach\'on | Sustainability of Transient Kinetic Regimes and Origins of Death | 11 pages, 5 figures | Sci. Rep. 6, 20562 (2016) | 10.1038/srep20562 | null | q-bio.MN nlin.AO physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is generally recognized that a distinguishing feature of life is its
peculiar capability to avoid equilibration. The origin of this capability and
its evolution along the timeline of abiogenesis is not yet understood. We
propose to study an analog of this phenomenon that could emerge in
non-biological systems. To this end, we introduce the concept of sustainability
of transient kinetic regimes. This concept is illustrated via investigation of
cooperative effects in an extended system of compartmentalized chemical
oscillators under batch and semi-batch conditions. The computational study of a
model system shows robust enhancement of lifetimes of the decaying oscillations
which translates into the evolution of the survival function of the transient
non-equilibrium regime. This model does not rely on any form of replication.
Rather, it explores the role of a structured effective environment as a
contributor to the system-bath interactions that define non-equilibrium
regimes. We implicate the noise produced by the effective environment of a
compartmentalized oscillator as the cause of the lifetime extension.
| [
{
"created": "Wed, 22 Jul 2015 12:44:57 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Jul 2015 04:45:04 GMT",
"version": "v2"
},
{
"created": "Tue, 12 Jan 2016 03:27:31 GMT",
"version": "v3"
}
] | 2016-10-13 | [
[
"Zubarev",
"Dmitry Yu.",
""
],
[
"Pachón",
"Leonardo A.",
""
]
] | It is generally recognized that a distinguishing feature of life is its peculiar capability to avoid equilibration. The origin of this capability and its evolution along the timeline of abiogenesis is not yet understood. We propose to study an analog of this phenomenon that could emerge in non-biological systems. To this end, we introduce the concept of sustainability of transient kinetic regimes. This concept is illustrated via investigation of cooperative effects in an extended system of compartmentalized chemical oscillators under batch and semi-batch conditions. The computational study of a model system shows robust enhancement of lifetimes of the decaying oscillations which translates into the evolution of the survival function of the transient non-equilibrium regime. This model does not rely on any form of replication. Rather, it explores the role of a structured effective environment as a contributor to the system-bath interactions that define non-equilibrium regimes. We implicate the noise produced by the effective environment of a compartmentalized oscillator as the cause of the lifetime extension. |
1307.0857 | Yi Ming Zou | Yi Ming Zou | Characterization of Boolean Networks with Single or Bistable States | Main results of this article appeared as a 4 page abstract in the
ICBBE 2012 Conference Proceeding, pp. 517-520 | null | null | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many biological systems, such as metabolic pathways, exhibit bistability
behavior: these biological systems exhibit two distinct stable states with
switching between the two stable states controlled by certain conditions. Since
understanding bistability is key for understanding these biological systems,
mathematical modeling of the bistability phenomenon has been at the focus of
researches in quantitative and system biology. Recent study shows that Boolean
networks offer relative simple mathematical models that are capable of
capturing these essential information. Thus a better understanding of the
Boolean networks with bistability property is desirable for both theoretical
and application purposes. In this paper, we describe an algebraic condition for
the number of stable states (fixed points) of a Boolean network based on its
polynomial representation, and derive algorithms for a Boolean network to have
a single stable state or two stable states. As an example, we also construct a
Boolean network with exactly two stable states for the lac operon's
$\beta$-galactosidase regulatory pathway when glucose is absent based on a
delay differential equation model
| [
{
"created": "Tue, 2 Jul 2013 21:36:40 GMT",
"version": "v1"
}
] | 2013-07-04 | [
[
"Zou",
"Yi Ming",
""
]
] | Many biological systems, such as metabolic pathways, exhibit bistability behavior: these biological systems exhibit two distinct stable states with switching between the two stable states controlled by certain conditions. Since understanding bistability is key for understanding these biological systems, mathematical modeling of the bistability phenomenon has been at the focus of researches in quantitative and system biology. Recent study shows that Boolean networks offer relative simple mathematical models that are capable of capturing these essential information. Thus a better understanding of the Boolean networks with bistability property is desirable for both theoretical and application purposes. In this paper, we describe an algebraic condition for the number of stable states (fixed points) of a Boolean network based on its polynomial representation, and derive algorithms for a Boolean network to have a single stable state or two stable states. As an example, we also construct a Boolean network with exactly two stable states for the lac operon's $\beta$-galactosidase regulatory pathway when glucose is absent based on a delay differential equation model |
1806.01416 | Arno Siri-J\'egousse | Asger Hobolth and Arno Siri-J\'egousse and Mogens Bladt | Phase-type distributions in population genetics | null | null | null | null | q-bio.PE q-bio.QM stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probability modelling for DNA sequence evolution is well established and
provides a rich framework for understanding genetic variation between samples
of individuals from one or more populations. We show that both classical and
more recent models for coalescence (with or without recombination) can be
described in terms of the so-called phase-type theory, where complicated and
tedious calculations are circumvented by the use of matrices. The application
of phase-type theory consists of describing the stochastic model as a Markov
model by appropriately setting up a state space and calculating the
corresponding intensity and reward matrices. Formulae of interest are then
expressed in terms of these aforementioned matrices. We illustrate this by a
few examples calculating the mean, variance and even higher order moments of
the site frequency spectrum in the multiple merger coalescent models, and by
analysing the mean and variance for the number of segregating sites for
multiple samples in the two-locus ancestral recombination graph. We believe
that phase-type theory has great potential as a tool for analysing probability
models in population genetics. The compact matrix notation is useful for
clarification of current models, in particular their formal manipulation
(calculation), but also for further development or extensions.
| [
{
"created": "Mon, 4 Jun 2018 22:22:10 GMT",
"version": "v1"
}
] | 2018-06-07 | [
[
"Hobolth",
"Asger",
""
],
[
"Siri-Jégousse",
"Arno",
""
],
[
"Bladt",
"Mogens",
""
]
] | Probability modelling for DNA sequence evolution is well established and provides a rich framework for understanding genetic variation between samples of individuals from one or more populations. We show that both classical and more recent models for coalescence (with or without recombination) can be described in terms of the so-called phase-type theory, where complicated and tedious calculations are circumvented by the use of matrices. The application of phase-type theory consists of describing the stochastic model as a Markov model by appropriately setting up a state space and calculating the corresponding intensity and reward matrices. Formulae of interest are then expressed in terms of these aforementioned matrices. We illustrate this by a few examples calculating the mean, variance and even higher order moments of the site frequency spectrum in the multiple merger coalescent models, and by analysing the mean and variance for the number of segregating sites for multiple samples in the two-locus ancestral recombination graph. We believe that phase-type theory has great potential as a tool for analysing probability models in population genetics. The compact matrix notation is useful for clarification of current models, in particular their formal manipulation (calculation), but also for further development or extensions. |
1902.06971 | Fran\c{c}ois Lavallee | Fran\c{c}ois Lavall\'ee, Charline Smadi, Isabelle Alvarez, Bj\"orn
Reineking, Fran\c{c}ois-Marie Martin, Fanny Dommanget, Sophie Martin | A stochastic individual based model for the growth of a stand of
Japanese knotweed including mowing as a management technique | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Invasive alien species are a growing threat for environment and health. They
also have a major economic impact, as they can damage many infrastructures. The
Japanese knotweed (Fallopia japonica), present in North America, Northern and
Central Europe as well as in Australia and New Zealand, is listed by the World
Conservation Union as one of the world's worst invasive species. So far, most
models have dealt with how the invasion spreads without management. This paper
aims at providing a model able to study and predict the dynamics of a stand of
Japanese knotweed taking into account mowing as a management technique. The
model we propose is stochastic and individual-based, which allows us taking
into account the behaviour of individuals depending on their size and location,
as well as individual stochasticity. We set plant dynamics parameters thanks to
a calibration with field data, and study the influence of the initial
population size, the mean number of mowing events a year and the management
project duration on mean area and mean number of crowns of stands. In
particular, our results provide the sets of parameters for which it is possible
to obtain the stand eradication, and the minimal duration of the management
project necessary to achieve this latter.
| [
{
"created": "Tue, 19 Feb 2019 09:47:42 GMT",
"version": "v1"
}
] | 2019-02-20 | [
[
"Lavallée",
"François",
""
],
[
"Smadi",
"Charline",
""
],
[
"Alvarez",
"Isabelle",
""
],
[
"Reineking",
"Björn",
""
],
[
"Martin",
"François-Marie",
""
],
[
"Dommanget",
"Fanny",
""
],
[
"Martin",
"Sophie",
""
]
] | Invasive alien species are a growing threat for environment and health. They also have a major economic impact, as they can damage many infrastructures. The Japanese knotweed (Fallopia japonica), present in North America, Northern and Central Europe as well as in Australia and New Zealand, is listed by the World Conservation Union as one of the world's worst invasive species. So far, most models have dealt with how the invasion spreads without management. This paper aims at providing a model able to study and predict the dynamics of a stand of Japanese knotweed taking into account mowing as a management technique. The model we propose is stochastic and individual-based, which allows us taking into account the behaviour of individuals depending on their size and location, as well as individual stochasticity. We set plant dynamics parameters thanks to a calibration with field data, and study the influence of the initial population size, the mean number of mowing events a year and the management project duration on mean area and mean number of crowns of stands. In particular, our results provide the sets of parameters for which it is possible to obtain the stand eradication, and the minimal duration of the management project necessary to achieve this latter. |
2209.05377 | Nicholas Glykos | Antonios Kolocouris, Isaiah Arkin and Nicholas M. Glykos | Folding Molecular Dynamics Simulations of the Transmembrane Peptides of
Influenza A, B M2, and MERS-, SARS-CoV E Viral Proteins | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Viroporins are small viral proteins that oligomerize in the membrane of host
cells and induce the formation of hydrophilic pores in these membranes, thus
altering the physiological properties of the host cells. Due to their
significance for viral pathogenicity, they have become targets for
pharmaceutical intervention, especially through compounds that block their
pore-forming activity. Here we add to the growing literature concerning the
structure and function of viroporins by studying and comparing -- through
molecular dynamics simulations -- the folding of the transmembrane domain
peptides of viroporins derived from four viruses : influenza A, influenza B,
and the coronaviruses MERS-Cov-2 and SARS-CoV-2. Through a total of more than
50 {\mu}s of simulation time in explicit solvent (TFE) and with full
electrostatics, we characterize the folding behavior, helical stability and
helical propensity of these transmembrane peptides in their monomeric state and
we identify common motifs that may reflect their quaternary organization and/or
biological function. We show that the two influenza-derived peptides are
significantly different in peptide sequence and secondary structure from the
two coronavirus-derived peptides, and that they are organized in two
structurally distinct parts : a significantly more stable N-terminal half, and
a fast converting C-terminal half that continuously folds and unfolds between
$\alpha$-helical structures and non-canonical structures which are mostly
turns. In contrast, the two coronavirus-derived transmembrane peptides are much
more stable and fast helix formers when compared with the influenza ones. We
discuss possible interpretations of these findings and their putative
connection to the structural characteristics of the respective viroporins.
| [
{
"created": "Mon, 12 Sep 2022 16:30:24 GMT",
"version": "v1"
}
] | 2022-09-13 | [
[
"Kolocouris",
"Antonios",
""
],
[
"Arkin",
"Isaiah",
""
],
[
"Glykos",
"Nicholas M.",
""
]
] | Viroporins are small viral proteins that oligomerize in the membrane of host cells and induce the formation of hydrophilic pores in these membranes, thus altering the physiological properties of the host cells. Due to their significance for viral pathogenicity, they have become targets for pharmaceutical intervention, especially through compounds that block their pore-forming activity. Here we add to the growing literature concerning the structure and function of viroporins by studying and comparing -- through molecular dynamics simulations -- the folding of the transmembrane domain peptides of viroporins derived from four viruses : influenza A, influenza B, and the coronaviruses MERS-Cov-2 and SARS-CoV-2. Through a total of more than 50 {\mu}s of simulation time in explicit solvent (TFE) and with full electrostatics, we characterize the folding behavior, helical stability and helical propensity of these transmembrane peptides in their monomeric state and we identify common motifs that may reflect their quaternary organization and/or biological function. We show that the two influenza-derived peptides are significantly different in peptide sequence and secondary structure from the two coronavirus-derived peptides, and that they are organized in two structurally distinct parts : a significantly more stable N-terminal half, and a fast converting C-terminal half that continuously folds and unfolds between $\alpha$-helical structures and non-canonical structures which are mostly turns. In contrast, the two coronavirus-derived transmembrane peptides are much more stable and fast helix formers when compared with the influenza ones. We discuss possible interpretations of these findings and their putative connection to the structural characteristics of the respective viroporins. |
1402.5270 | Jinming Du | Jinming Du, Bin Wu, Philipp M. Altrock and Long Wang | Aspiration Dynamics of Multi-player Games in Finite Populations | null | null | 10.1098/rsif.2014.0077 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Studying strategy update rules in the framework of evolutionary game theory,
one can differentiate between imitation processes and aspiration-driven
dynamics. In the former case, individuals imitate the strategy of a more
successful peer. In the latter case, individuals adjust their strategies based
on a comparison of their payoffs from the evolutionary game to a value they
aspire, called the level of aspiration. Unlike imitation processes of pairwise
comparison, aspiration-driven updates do not require additional information
about the strategic environment and can thus be interpreted as being more
spontaneous. Recent work has mainly focused on understanding how aspiration
dynamics alter the evolutionary outcome in structured populations. However, the
baseline case for understanding strategy selection is the well-mixed population
case, which is still lacking sufficient understanding. We explore how
aspiration-driven strategy-update dynamics under imperfect rationality
influence the average abundance of a strategy in multi-player evolutionary
games with two strategies. We analytically derive a condition under which a
strategy is more abundant than the other in the weak selection limiting case.
This approach has a long standing history in evolutionary game and is mostly
applied for its mathematical approachability. Hence, we also explore strong
selection numerically, which shows that our weak selection condition is a
robust predictor of the average abundance of a strategy. The condition turns
out to differ from that of a wide class of imitation dynamics, as long as the
game is not dyadic. Therefore a strategy favored under imitation dynamics can
be disfavored under aspiration dynamics. This does not require any population
structure thus highlights the intrinsic difference between imitation and
aspiration dynamics.
| [
{
"created": "Fri, 21 Feb 2014 12:14:08 GMT",
"version": "v1"
}
] | 2014-03-06 | [
[
"Du",
"Jinming",
""
],
[
"Wu",
"Bin",
""
],
[
"Altrock",
"Philipp M.",
""
],
[
"Wang",
"Long",
""
]
] | Studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their payoffs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long standing history in evolutionary game and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore a strategy favored under imitation dynamics can be disfavored under aspiration dynamics. This does not require any population structure thus highlights the intrinsic difference between imitation and aspiration dynamics. |
1904.00997 | Natan T. Shaked | Darina Roitshtain, Lauren Wolbromsky, Evgeny Bal, Hayit Greenspan,
Lisa L. Satterwhite, and Natan T. Shaked | Quantitative phase microscopy spatial signatures of cancer cells | null | Cytometry A. 2017 May;91(5):482-493 | 10.1002/cyto.a.23100 | null | q-bio.QM physics.bio-ph q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present cytometric classification of live healthy and cancer cells by
using the spatial morphological and textural information found in the
label-free quantitative phase images of the cells. We compare both healthy
cells to primary tumor cell and primary tumor cells to metastatic cancer cells,
where tumor biopsies and normal tissues were isolated from the same
individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells
were imaged while unattached to the substrate. We used low-coherence off-axis
interferometric phase microscopy setup, which allows a single-exposure
acquisition mode, and thus is suitable for quantitative imaging of dynamic
cells during flow. After acquisition, the optical path delay maps of the cells
were extracted, and used to calculate 15 parameters derived from cellular 3-D
morphology and texture. Upon analyzing tens of cells in each group, we found
high statistical significance in the difference between the groups in most of
the parameters calculated, with the same trends for all statistically
significant parameters. Furthermore, a specially designed machine learning
algorithm, implemented on the phase map extracted features, classified the
correct cell type (healthy/cancer/metastatic) with 81%-93% sensitivity and
81%-99% specificity. The quantitative phase imaging approach for liquid
biopsies presented in this paper could be the basis for advanced techniques of
staging freshly isolated live cancer cells in imaging flow cytometers.
| [
{
"created": "Tue, 2 Apr 2019 14:27:04 GMT",
"version": "v1"
}
] | 2020-01-23 | [
[
"Roitshtain",
"Darina",
""
],
[
"Wolbromsky",
"Lauren",
""
],
[
"Bal",
"Evgeny",
""
],
[
"Greenspan",
"Hayit",
""
],
[
"Satterwhite",
"Lisa L.",
""
],
[
"Shaked",
"Natan T.",
""
]
] | We present cytometric classification of live healthy and cancer cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cell and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted, and used to calculate 15 parameters derived from cellular 3-D morphology and texture. Upon analyzing tens of cells in each group, we found high statistical significance in the difference between the groups in most of the parameters calculated, with the same trends for all statistically significant parameters. Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81%-93% sensitivity and 81%-99% specificity. The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers. |
1502.02027 | Mario Mulansky | Mario Mulansky, Nebojsa Bozanic, Andreea Sburlea and Thomas Kreuz | A guide to time-resolved and parameter-free measures of spike train
synchrony | 8 pages, 4 figures | Mulansky, Mario; Bozanic, Nebojsa; Sburlea, Andreea; Kreuz,
Thomas, 2015 International Conference on Event-based Control, Communication,
and Signal Processing (EBCCSP), pp.1-8, 17-19 June 2015 | 10.1109/EBCCSP.2015.7300693 | null | q-bio.NC physics.bio-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Measures of spike train synchrony have proven a valuable tool in both
experimental and computational neuroscience. Particularly useful are
time-resolved methods such as the ISI- and the SPIKE-distance, which have
already been applied in various bivariate and multivariate contexts. Recently,
SPIKE-Synchronization was proposed as another time-resolved synchronization
measure. It is based on Event-Synchronization and has a very intuitive
interpretation. Here, we present a detailed analysis of the mathematical
properties of these three synchronization measures. For example, we were able
to obtain analytic expressions for the expectation values of the ISI-distance
and SPIKE-Synchronization for Poisson spike trains. For the SPIKE-distance we
present an empirical formula deduced from numerical evaluations. These
expectation values are crucial for interpreting the synchronization of spike
trains measured in experiments or numerical simulations, as they represent the
point of reference for fully randomized spike trains.
| [
{
"created": "Thu, 5 Feb 2015 22:57:21 GMT",
"version": "v1"
},
{
"created": "Thu, 7 May 2015 19:01:51 GMT",
"version": "v2"
},
{
"created": "Fri, 6 Nov 2015 02:54:39 GMT",
"version": "v3"
}
] | 2015-11-09 | [
[
"Mulansky",
"Mario",
""
],
[
"Bozanic",
"Nebojsa",
""
],
[
"Sburlea",
"Andreea",
""
],
[
"Kreuz",
"Thomas",
""
]
] | Measures of spike train synchrony have proven a valuable tool in both experimental and computational neuroscience. Particularly useful are time-resolved methods such as the ISI- and the SPIKE-distance, which have already been applied in various bivariate and multivariate contexts. Recently, SPIKE-Synchronization was proposed as another time-resolved synchronization measure. It is based on Event-Synchronization and has a very intuitive interpretation. Here, we present a detailed analysis of the mathematical properties of these three synchronization measures. For example, we were able to obtain analytic expressions for the expectation values of the ISI-distance and SPIKE-Synchronization for Poisson spike trains. For the SPIKE-distance we present an empirical formula deduced from numerical evaluations. These expectation values are crucial for interpreting the synchronization of spike trains measured in experiments or numerical simulations, as they represent the point of reference for fully randomized spike trains. |
1811.09619 | Bianca-Cristina Cristescu | Bianca-Cristina Cristescu, Zal\'an Borsos, John Lygeros, Mar\'ia
Rodr\'iguez Mart\'inez, Maria Anna Rapsomaniki | Inference of the three-dimensional chromatin structure and its temporal
behavior | 10 pages, 7 figures, 1 algorithm. Neural Information Processing
Systems, Machine Learning for Molecules and Materials, 2018 | null | null | null | q-bio.GN cs.LG q-bio.QM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the three-dimensional (3D) structure of the genome is essential
for elucidating vital biological processes and their links to human disease. To
determine how the genome folds within the nucleus, chromosome conformation
capture methods such as HiC have recently been employed. However, computational
methods that exploit the resulting high-throughput, high-resolution data are
still suffering from important limitations. In this work, we explore the idea
of manifold learning for the 3D chromatin structure inference and present a
novel method, REcurrent Autoencoders for CHromatin 3D structure prediction
(REACH-3D). Our framework employs autoencoders with recurrent neural units to
reconstruct the chromatin structure. In comparison to existing methods,
REACH-3D makes no transfer function assumption and permits dynamic analysis.
Evaluating REACH-3D on synthetic data indicated high agreement with the ground
truth. When tested on real experimental HiC data, REACH-3D recovered most
faithfully the expected biological properties and obtained the highest
correlation coefficient with microscopy measurements. Last, REACH-3D was
applied to dynamic HiC data, where it successfully modeled chromatin
conformation during the cell cycle.
| [
{
"created": "Thu, 22 Nov 2018 15:19:33 GMT",
"version": "v1"
}
] | 2018-11-27 | [
[
"Cristescu",
"Bianca-Cristina",
""
],
[
"Borsos",
"Zalán",
""
],
[
"Lygeros",
"John",
""
],
[
"Martínez",
"María Rodríguez",
""
],
[
"Rapsomaniki",
"Maria Anna",
""
]
] | Understanding the three-dimensional (3D) structure of the genome is essential for elucidating vital biological processes and their links to human disease. To determine how the genome folds within the nucleus, chromosome conformation capture methods such as HiC have recently been employed. However, computational methods that exploit the resulting high-throughput, high-resolution data are still suffering from important limitations. In this work, we explore the idea of manifold learning for the 3D chromatin structure inference and present a novel method, REcurrent Autoencoders for CHromatin 3D structure prediction (REACH-3D). Our framework employs autoencoders with recurrent neural units to reconstruct the chromatin structure. In comparison to existing methods, REACH-3D makes no transfer function assumption and permits dynamic analysis. Evaluating REACH-3D on synthetic data indicated high agreement with the ground truth. When tested on real experimental HiC data, REACH-3D recovered most faithfully the expected biological properties and obtained the highest correlation coefficient with microscopy measurements. Last, REACH-3D was applied to dynamic HiC data, where it successfully modeled chromatin conformation during the cell cycle. |
1803.07886 | Stephen Smith | Stephen Smith and Neil Dalchau | Beyond activator-inhibitor networks: the generalised Turing mechanism | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Turing patterning mechanism is believed to underly the formation of
repetitive structures in development, such as zebrafish stripes and mammalian
digits, but it has proved difficult to isolate the specific biochemical species
responsible for pattern formation. Meanwhile, synthetic biologists have
designed Turing systems for implementation in cell colonies, but none have yet
led to visible patterns in the laboratory. In both cases, the relationship
between underlying chemistry and emergent biology remains mysterious. To help
resolve the mystery, this article asks the question: what kinds of biochemical
systems can generate Turing patterns? We find general conditions for Turing
pattern inception -- the ability to generate unstable patterns from random
noise -- which may lead to the ultimate formation of stable patterns, depending
on biochemical non-linearities. We find that a wide variety of systems can
generate stable Turing patterns, including several which are currently unknown,
such as two-species systems composed of two self-activators, and systems
composed of a short-range inhibitor and a long-range activator. We furthermore
find that systems which are widely believed to generate stable patterns may in
fact only generate unstable patterns, which ultimately converge to
spatially-homogeneous concentrations. Our results suggest that a much wider
variety of systems than is commonly believed could be responsible for observed
patterns in development, or could be good candidates for synthetic patterning
networks.
| [
{
"created": "Wed, 21 Mar 2018 12:33:28 GMT",
"version": "v1"
}
] | 2018-03-22 | [
[
"Smith",
"Stephen",
""
],
[
"Dalchau",
"Neil",
""
]
] | The Turing patterning mechanism is believed to underly the formation of repetitive structures in development, such as zebrafish stripes and mammalian digits, but it has proved difficult to isolate the specific biochemical species responsible for pattern formation. Meanwhile, synthetic biologists have designed Turing systems for implementation in cell colonies, but none have yet led to visible patterns in the laboratory. In both cases, the relationship between underlying chemistry and emergent biology remains mysterious. To help resolve the mystery, this article asks the question: what kinds of biochemical systems can generate Turing patterns? We find general conditions for Turing pattern inception -- the ability to generate unstable patterns from random noise -- which may lead to the ultimate formation of stable patterns, depending on biochemical non-linearities. We find that a wide variety of systems can generate stable Turing patterns, including several which are currently unknown, such as two-species systems composed of two self-activators, and systems composed of a short-range inhibitor and a long-range activator. We furthermore find that systems which are widely believed to generate stable patterns may in fact only generate unstable patterns, which ultimately converge to spatially-homogeneous concentrations. Our results suggest that a much wider variety of systems than is commonly believed could be responsible for observed patterns in development, or could be good candidates for synthetic patterning networks. |
2112.03972 | Wan Yang | Wan Yang and Jeffrey Shaman | Viral replication dynamics could critically modulate vaccine
effectiveness and should be accounted for when assessing new SARS-CoV-2
variants | 3 pages, 1 figure | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | In this article, we propose a theory to explain the reduction in vaccine
effectiveness (VE) against the Delta SARS-CoV-2 variant and decreasing VE over
time reported in recent studies. Using a model illustration, we show that
in-host viral replication dynamics and delays in immune response could play a
key role in VE. Given this, current laboratory approaches solely measuring
reductions in neutralizing ability cannot fully represent the potential impact
of new SARS-CoV-2 variants. We instead propose an alternative approach that
incorporates viral replication dynamics into evaluations of SARS-CoV-2 variant
impact on immunity and VE. This more robust assessment may better inform public
health response to new variants like the newly detected Omicron variant.
| [
{
"created": "Tue, 7 Dec 2021 20:21:03 GMT",
"version": "v1"
}
] | 2021-12-09 | [
[
"Yang",
"Wan",
""
],
[
"Shaman",
"Jeffrey",
""
]
] | In this article, we propose a theory to explain the reduction in vaccine effectiveness (VE) against the Delta SARS-CoV-2 variant and decreasing VE over time reported in recent studies. Using a model illustration, we show that in-host viral replication dynamics and delays in immune response could play a key role in VE. Given this, current laboratory approaches solely measuring reductions in neutralizing ability cannot fully represent the potential impact of new SARS-CoV-2 variants. We instead propose an alternative approach that incorporates viral replication dynamics into evaluations of SARS-CoV-2 variant impact on immunity and VE. This more robust assessment may better inform public health response to new variants like the newly detected Omicron variant. |
2303.04863 | Fernando Rosas | Fernando E. Rosas, Diego Candia-Rivera, Andrea I Luppi, Yike Guo,
Pedro A.M. Mediano | Bayesian at heart: Towards autonomic outflow estimation via generative
state-space modelling of heart rate dynamics | 12 pages, 5 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research is revealing how cognitive processes are supported by a
complex interplay between the brain and the rest of the body, which can be
investigated by the analysis of physiological features such as breathing
rhythms, heart rate, and skin conductance. Heart rate dynamics are of
particular interest as they provide a way to track the sympathetic and
parasympathetic outflow from the autonomic nervous system, which is known to
play a key role in modulating attention, memory, decision-making, and emotional
processing. However, extracting useful information from heartbeats about the
autonomic outflow is still challenging due to the noisy estimates that result
from standard signal-processing methods. To advance this state of affairs, we
propose a paradigm shift in how we conceptualise and model heart rate: instead
of being a mere summary of the observed inter-beat intervals, we introduce a
modelling framework that views heart rate as a hidden stochastic process that
drives the observed heartbeats. Moreover, by leveraging the rich literature of
state-space modelling and Bayesian inference, our proposed framework delivers a
description of heart rate dynamics that is not a point estimate but a posterior
distribution of a generative model. We illustrate the capabilities of our
method by showing that it recapitulates linear properties of conventional heart
rate estimators, while exhibiting a better discriminative power for metrics of
dynamical complexity compared across different physiological states.
| [
{
"created": "Wed, 8 Mar 2023 20:05:34 GMT",
"version": "v1"
}
] | 2023-03-10 | [
[
"Rosas",
"Fernando E.",
""
],
[
"Candia-Rivera",
"Diego",
""
],
[
"Luppi",
"Andrea I",
""
],
[
"Guo",
"Yike",
""
],
[
"Mediano",
"Pedro A. M.",
""
]
] | Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a paradigm shift in how we conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states. |
2402.11459 | Yufei Huang | Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang
Gao, Siyuan Li and Stan.Z. Li | Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion
Bridge | null | null | null | null | q-bio.BM cs.AI cs.LG physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | Accurate prediction of protein-ligand binding structures, a task known as
molecular docking is crucial for drug design but remains challenging. While
deep learning has shown promise, existing methods often depend on holo-protein
structures (docked, and not accessible in realistic tasks) or neglect pocket
sidechain conformations, leading to limited practical utility and unrealistic
conformation predictions. To fill these gaps, we introduce an under-explored
task, named flexible docking to predict poses of ligand and pocket sidechains
simultaneously and introduce Re-Dock, a novel diffusion bridge generative model
extended to geometric manifolds. Specifically, we propose energy-to-geometry
mapping inspired by the Newton-Euler equation to co-model the binding energy
and conformations for reflecting the energy-constrained docking generative
process. Comprehensive experiments on designed benchmark datasets including
apo-dock and cross-dock demonstrate our model's superior effectiveness and
efficiency over current methods.
| [
{
"created": "Sun, 18 Feb 2024 05:04:50 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Feb 2024 07:46:07 GMT",
"version": "v2"
}
] | 2024-02-22 | [
[
"Huang",
"Yufei",
""
],
[
"Zhang",
"Odin",
""
],
[
"Wu",
"Lirong",
""
],
[
"Tan",
"Cheng",
""
],
[
"Lin",
"Haitao",
""
],
[
"Gao",
"Zhangyang",
""
],
[
"Li",
"Siyuan",
""
],
[
"Li",
"Stan. Z.",
""
]
] | Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging. While deep learning has shown promise, existing methods often depend on holo-protein structures (docked, and not accessible in realistic tasks) or neglect pocket sidechain conformations, leading to limited practical utility and unrealistic conformation predictions. To fill these gaps, we introduce an under-explored task, named flexible docking to predict poses of ligand and pocket sidechains simultaneously and introduce Re-Dock, a novel diffusion bridge generative model extended to geometric manifolds. Specifically, we propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations for reflecting the energy-constrained docking generative process. Comprehensive experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods. |
1904.06712 | Birgitta Dresp-Langley | Birgitta Dresp-Langley | Bilateral symmetry strengthens the perceptual salience of figure against
ground | null | 2019, Symmetry, 11(2), 225 | 10.3390/sym11020225 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although symmetry has been discussed in terms of a major law of perceptual
organization since the early conceptual efforts of the Gestalt school
(Wertheimer, Metzger, Koffka and others), the first quantitative measurements
testing for effects of symmetry on processes of Gestalt formation have seen the
day only recently. In this study, a psychophysical rating study and a
"foreground" versus "background" choice response time experiment were run with
human observers to test for effects of bilateral symmetry on the perceived
strength of figure against ground in triangular Kanizsa configurations.
Displays with and without bilateral symmetry, identical physically specified to
total contour ratio and constant local contrast intensity within and across
conditions, but variable local contrast polarity and variable orientation in
the plane were presented in a random order to human observers. Configurations
with bilateral symmetry produced significantly stronger figure against ground
percepts reflected by greater subjective magnitudes and consistently higher
percentages of "foreground" judgments accompanied by significantly shorter
response times. These effects of symmetry depend neither on the orientation of
the axis of symmetry, nor on the contrast polarity of the physical inducers. It
is concluded that bilateral symmetry, irrespective of orientation,
significantly contributes to the, largely sign invariant, visual mechanisms of
shape segregation that determine the salience of figure against ground in
perceptually ambiguous image configurations.
| [
{
"created": "Sun, 14 Apr 2019 15:46:37 GMT",
"version": "v1"
}
] | 2019-04-16 | [
[
"Dresp-Langley",
"Birgitta",
""
]
] | Although symmetry has been discussed in terms of a major law of perceptual organization since the early conceptual efforts of the Gestalt school (Wertheimer, Metzger, Koffka and others), the first quantitative measurements testing for effects of symmetry on processes of Gestalt formation have seen the day only recently. In this study, a psychophysical rating study and a "foreground" versus "background" choice response time experiment were run with human observers to test for effects of bilateral symmetry on the perceived strength of figure against ground in triangular Kanizsa configurations. Displays with and without bilateral symmetry, identical physically specified to total contour ratio and constant local contrast intensity within and across conditions, but variable local contrast polarity and variable orientation in the plane were presented in a random order to human observers. Configurations with bilateral symmetry produced significantly stronger figure against ground percepts reflected by greater subjective magnitudes and consistently higher percentages of "foreground" judgments accompanied by significantly shorter response times. These effects of symmetry depend neither on the orientation of the axis of symmetry, nor on the contrast polarity of the physical inducers. It is concluded that bilateral symmetry, irrespective of orientation, significantly contributes to the, largely sign invariant, visual mechanisms of shape segregation that determine the salience of figure against ground in perceptually ambiguous image configurations. |
2205.02075 | Markus Meister | Markus Meister | Learning, fast and slow | Submitted to Current Opinion in Neurobiology | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Animals can learn efficiently from a single experience and change their
future behavior in response. However, in other instances, animals learn very
slowly, requiring thousands of experiences. Here I survey tasks involving fast
and slow learning and consider some hypotheses for what differentiates the
underlying neural mechanisms. It has been proposed that fast learning relies on
neural representations that favor efficient Hebbian modification of synapses.
These efficient representations may be encoded in the genome, resulting in a
repertoire of fast learning that differs across species. Alternatively, the
required neural representations may be acquired from experience through a slow
process of unsupervised learning from the environment.
| [
{
"created": "Wed, 6 Apr 2022 19:16:33 GMT",
"version": "v1"
}
] | 2022-05-05 | [
[
"Meister",
"Markus",
""
]
] | Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural mechanisms. It has been proposed that fast learning relies on neural representations that favor efficient Hebbian modification of synapses. These efficient representations may be encoded in the genome, resulting in a repertoire of fast learning that differs across species. Alternatively, the required neural representations may be acquired from experience through a slow process of unsupervised learning from the environment. |
1602.01191 | Ganesh Bagler Dr | Megha Singh, Rahul Badhwar and Ganesh Bagler | Network biomarkers of schizophrenia by graph theoretical investigations
of Brain Functional Networks | 9 pages (inclusive of Supplementary Material), 5 figures. Updated: to
modify preprocessing steps; to reorganize results, abstract and discussion;
to include new author; to include new affiliation; and to include relevant
references | null | null | IITJ/SEED/2014/0003 | q-bio.QM physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brain Functional Networks (BFNs), graph theoretical models of brain activity
data, provide a systems perspective of complex functional connectivity within
the brain. Neurological disorders are known to have basis in abnormal
functional activities, that could be captured in terms of network markers.
Schizophrenia is a pathological condition characterized with altered brain
functional state. We created weighted and binary BFN models of schizophrenia
patients as well as healthy subjects starting from fMRI data in an effort to
search for network biomarkers of the disease. We investigated 45 topological
features of BFNs and their higher order combinations (2 to 8). We find that
network features embodying modularity, betweenness, assortativity and edge
density emerge as key markers of schizophrenia. Also, features derived from
weighted BFNs were observed to be more effective in disease classification as
compared to those from binary BFNs. These topological markers may provide
insights into mechanisms of functional activity underlying disease phenotype
and could further be used for designing algorithms for clinical diagnosis of
schizophrenia as well as its early detection.
| [
{
"created": "Wed, 3 Feb 2016 05:20:43 GMT",
"version": "v1"
},
{
"created": "Sat, 27 Aug 2016 08:35:58 GMT",
"version": "v2"
}
] | 2016-08-30 | [
[
"Singh",
"Megha",
""
],
[
"Badhwar",
"Rahul",
""
],
[
"Bagler",
"Ganesh",
""
]
] | Brain Functional Networks (BFNs), graph theoretical models of brain activity data, provide a systems perspective of complex functional connectivity within the brain. Neurological disorders are known to have basis in abnormal functional activities, that could be captured in terms of network markers. Schizophrenia is a pathological condition characterized with altered brain functional state. We created weighted and binary BFN models of schizophrenia patients as well as healthy subjects starting from fMRI data in an effort to search for network biomarkers of the disease. We investigated 45 topological features of BFNs and their higher order combinations (2 to 8). We find that network features embodying modularity, betweenness, assortativity and edge density emerge as key markers of schizophrenia. Also, features derived from weighted BFNs were observed to be more effective in disease classification as compared to those from binary BFNs. These topological markers may provide insights into mechanisms of functional activity underlying disease phenotype and could further be used for designing algorithms for clinical diagnosis of schizophrenia as well as its early detection. |
1711.04393 | Unjong Yu | Jeong-Ok Choi and Unjong Yu | Fixation probability on clique-based graphs | 9 pages, 4 figures | null | 10.1016/j.physa.2017.11.131 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fixation probability of a mutant in the evolutionary dynamics of Moran
process is calculated by the Monte-Carlo method on a few families of
clique-based graphs. It is shown that the complete suppression of fixation can
be realized with the generalized clique-wheel graph in the limit of small
wheel-clique ratio and infinite size. The family of clique-star is an
amplifier, and clique-arms graph changes from amplifier to suppressor as the
fitness of the mutant increases. We demonstrate that the overall structure of a
graph can be more important to determine the fixation probability than the
degree or the heat heterogeneity. The dependence of the fixation probability on
the position of the first mutant is discussed.
| [
{
"created": "Mon, 13 Nov 2017 02:29:36 GMT",
"version": "v1"
}
] | 2018-01-17 | [
[
"Choi",
"Jeong-Ok",
""
],
[
"Yu",
"Unjong",
""
]
] | The fixation probability of a mutant in the evolutionary dynamics of Moran process is calculated by the Monte-Carlo method on a few families of clique-based graphs. It is shown that the complete suppression of fixation can be realized with the generalized clique-wheel graph in the limit of small wheel-clique ratio and infinite size. The family of clique-star is an amplifier, and clique-arms graph changes from amplifier to suppressor as the fitness of the mutant increases. We demonstrate that the overall structure of a graph can be more important to determine the fixation probability than the degree or the heat heterogeneity. The dependence of the fixation probability on the position of the first mutant is discussed. |
1307.3856 | Sriganesh Srihari Dr | Phi Vu Nguyen, Sriganesh Srihari and Hon Wai Leong | Identifying conserved protein complexes between species by constructing
interolog networks | 42 pages, 10 Tables, 11 Figures. To appear in BMC Bioinformatics
InCoB 2013 Supplement | BMC Bioinformatics 14(Suppl 16):S8 2013 | 10.1186/1471-2105-14-S16-S8 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein complexes conserved across species indicate processes that are core
to cellular machinery (e.g. cell-cycle or DNA damage-repair complexes conserved
across human and yeast). While numerous computational methods have been devised
to identify complexes from the protein interaction (PPI) networks of individual
species, these are severely limited by noise and errors (false positives) in
currently available datasets. Our analysis using human and yeast PPI networks
revealed that these methods missed several important complexes including those
conserved between the two species (e.g. the MLH1-MSH2-PMS2-PCNA mismatch-repair
complex). Here, we note that much of the functionalities of yeast complexes
have been conserved in human complexes not only through sequence conservation
of proteins but also of critical functional domains. Therefore, integrating
information of domain conservation might throw further light on conservation
patterns between yeast and human complexes.
| [
{
"created": "Mon, 15 Jul 2013 09:07:22 GMT",
"version": "v1"
}
] | 2013-10-24 | [
[
"Nguyen",
"Phi Vu",
""
],
[
"Srihari",
"Sriganesh",
""
],
[
"Leong",
"Hon Wai",
""
]
] | Protein complexes conserved across species indicate processes that are core to cellular machinery (e.g. cell-cycle or DNA damage-repair complexes conserved across human and yeast). While numerous computational methods have been devised to identify complexes from the protein interaction (PPI) networks of individual species, these are severely limited by noise and errors (false positives) in currently available datasets. Our analysis using human and yeast PPI networks revealed that these methods missed several important complexes including those conserved between the two species (e.g. the MLH1-MSH2-PMS2-PCNA mismatch-repair complex). Here, we note that much of the functionalities of yeast complexes have been conserved in human complexes not only through sequence conservation of proteins but also of critical functional domains. Therefore, integrating information of domain conservation might throw further light on conservation patterns between yeast and human complexes. |
2206.00611 | Md Masud Rana | Md Masud Rana and Duc Duy Nguyen | EISA-Score: Element Interactive Surface Area Score for Protein-Ligand
Binding Affinity Prediction | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Molecular surface representations have been advertised as a great tool to
study protein structure and functions, including protein-ligand binding
affinity modeling. However, the conventional surface-area-based methods fail to
deliver a competitive performance on the energy scoring tasks. The main reason
is the lack of crucial physical and chemical interactions encoded in the
molecular surface generations. We present novel molecular surface
representations embedded in different scales of the element interactive
manifolds featuring the dramatically dimensional reduction and accurately
physical and biological properties encoders. Those low-dimensional
surface-based descriptors are ready to be paired with any advanced machine
learning algorithms to explore the essential structure-activity relationships
that give rise to the element interactive surface area-based scoring functions
(EISA-score). The newly developed EISA-score has outperformed many
state-of-the-art models, including various well-established surface-related
representations, in standard PDBbind benchmarks.
| [
{
"created": "Wed, 1 Jun 2022 16:31:45 GMT",
"version": "v1"
}
] | 2022-06-02 | [
[
"Rana",
"Md Masud",
""
],
[
"Nguyen",
"Duc Duy",
""
]
] | Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks. |
1804.11078 | Jicun Wang-Michelitsch | Jicun Wang-Michelitsch, Thomas M Michelitsch | Pediatric lymphoma may develop by "one-step" cell transformation of a
lymphoid cell | 28 pages, 2 figures | null | null | null | q-bio.CB q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lymphomas are a large group of neoplasms developed from lymphoid cells (LCs)
in lymph nodes (LNs) or lymphoid tissues (LTs). Some forms of lymphomas,
including Burkitt lymphoma (BL), ALK+ anaplastic large cell lymphoma
(ALK+-ALCL), and T-cell lymphoblastic lymphoma/leukemia (T-LBL), occur mainly
in children and teenagers. Hodgkin's lymphoma (HL) has a peak incidence at age
20s. To understand pediatric lymphoma, we have recently proposed two hypotheses
on the causes and the mechanism of cell transformation of a LC. Hypothesis A
is: repeated bone-remodeling during bone-growth and bone-repair may be a source
of cell injuries of marrow cells including hematopoietic stem cells (HSCs),
myeloid cells, and LCs, and thymic involution may be a source of damage to the
developing T-cells in thymus. Hypothesis B is: a LC may have three pathways on
transformation: a slow, a rapid, and an accelerated. In this paper, we discuss
pediatric lymphomas by this hypothesis. Having a peak incidence at young age,
BL, T-LBL, ALK+-ALCL, and HL develop more likely as a result of rapid
transformation of a LC. In BL, ALK+-ALCL, and HL, the cell transformations may
be triggered by severe viral infections. In T-LBL, the cell transformation may
be related to thymic involution. Occurring in both adults and children, diffuse
large B-cell lymphoma (DLBCL) may develop via slow or accelerated pathway. In
conclusion, pediatric lymphoma may develop as a result of "one-step" cell
transformation of a LC, and severe viral infections may be the main trigger for
the rapid transformation of a LC in a LN/LT.
| [
{
"created": "Mon, 30 Apr 2018 08:32:20 GMT",
"version": "v1"
}
] | 2018-05-01 | [
[
"Wang-Michelitsch",
"Jicun",
""
],
[
"Michelitsch",
"Thomas M",
""
]
] | Lymphomas are a large group of neoplasms developed from lymphoid cells (LCs) in lymph nodes (LNs) or lymphoid tissues (LTs). Some forms of lymphomas, including Burkitt lymphoma (BL), ALK+ anaplastic large cell lymphoma (ALK+-ALCL), and T-cell lymphoblastic lymphoma/leukemia (T-LBL), occur mainly in children and teenagers. Hodgkin's lymphoma (HL) has a peak incidence at age 20s. To understand pediatric lymphoma, we have recently proposed two hypotheses on the causes and the mechanism of cell transformation of a LC. Hypothesis A is: repeated bone-remodeling during bone-growth and bone-repair may be a source of cell injuries of marrow cells including hematopoietic stem cells (HSCs), myeloid cells, and LCs, and thymic involution may be a source of damage to the developing T-cells in thymus. Hypothesis B is: a LC may have three pathways on transformation: a slow, a rapid, and an accelerated. In this paper, we discuss pediatric lymphomas by this hypothesis. Having a peak incidence at young age, BL, T-LBL, ALK+-ALCL, and HL develop more likely as a result of rapid transformation of a LC. In BL, ALK+-ALCL, and HL, the cell transformations may be triggered by severe viral infections. In T-LBL, the cell transformation may be related to thymic involution. Occurring in both adults and children, diffuse large B-cell lymphoma (DLBCL) may develop via slow or accelerated pathway. In conclusion, pediatric lymphoma may develop as a result of "one-step" cell transformation of a LC, and severe viral infections may be the main trigger for the rapid transformation of a LC in a LN/LT. |
1708.08135 | Zixuan Cang | Zixuan Cang, Lin Mu, Guowei Wei | Representability of algebraic topology for biomolecules in machine
learning based scoring and virtual screening | null | null | 10.1371/journal.pcbi.1005929 | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work introduces a number of algebraic topology approaches, such as
multicomponent persistent homology, multi-level persistent homology and
electrostatic persistence for the representation, characterization, and
description of small molecules and biomolecular complexes. Multicomponent
persistent homology retains critical chemical and biological information during
the topological simplification of biomolecular geometric complexity.
Multi-level persistent homology enables a tailored topological description of
inter- and/or intra-molecular interactions of interest. Electrostatic
persistence incorporates partial charge information into topological
invariants. These topological methods are paired with Wasserstein distance to
characterize similarities between molecules and are further integrated with a
variety of machine learning algorithms, including k-nearest neighbors, ensemble
of trees, and deep convolutional neural networks, to manifest their descriptive
and predictive powers for chemical and biological problems. Extensive numerical
experiments involving more than 4,000 protein-ligand complexes from the PDBBind
database and near 100,000 ligands and decoys in the DUD database are performed
to test respectively the scoring power and the virtual screening power of the
proposed topological approaches. It is demonstrated that the present approaches
outperform the modern machine learning based methods in protein-ligand binding
affinity predictions and ligand-decoy discrimination.
| [
{
"created": "Sun, 27 Aug 2017 20:41:14 GMT",
"version": "v1"
}
] | 2018-02-07 | [
[
"Cang",
"Zixuan",
""
],
[
"Mu",
"Lin",
""
],
[
"Wei",
"Guowei",
""
]
] | This work introduces a number of algebraic topology approaches, such as multicomponent persistent homology, multi-level persistent homology and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. Multicomponent persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for chemical and biological problems. Extensive numerical experiments involving more than 4,000 protein-ligand complexes from the PDBBind database and near 100,000 ligands and decoys in the DUD database are performed to test respectively the scoring power and the virtual screening power of the proposed topological approaches. It is demonstrated that the present approaches outperform the modern machine learning based methods in protein-ligand binding affinity predictions and ligand-decoy discrimination. |
1911.09415 | Reza Mahini Sheikhhosseini | Reza Mahini, Peng Xu, Guoliang Chen, Yansong Li, Weiyan Ding, Lei
Zhang, Nauman Khalid Qureshi, Asoke K. Nandi, Fengyu Cong | Optimal Number of Clusters by Measuring Similarity among Topographies
for Spatio-temporal ERP Analysis | 34 Pages, 15 figures, 9 tables, under review in Brain Topography | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Averaging amplitudes over consecutive time samples within a time-window is
widely used to calculate the amplitude of an event-related potential (ERP) for
cognitive neuroscience. Objective determination of the time-window is critical
for determining the ERP component. Clustering on the spatio-temporal ERP data
can obtain the time-window in which the consecutive time samples topographies
are expected to be highly similar in practice. However, there exists a
challenging problem of determining an optimal number of clusters. Here, we
develop a novel methodology to obtain the optimal number of clusters using
consensus clustering on the spatio-temporal ERP data. Various clustering
methods, namely, K-means, hierarchical clustering, fuzzy C-means,
self-organizing map, and diffusion maps spectral clustering are combined in an
ensemble clustering manner to find the most reliable clusters. When a range of
numbers of clusters is applied on the spatio-temporal ERP dataset, the optimal
number of clusters should correspond to the cluster of interest within which
the average of correlation coefficients between topographies of every two-time
sample in the time-window is the maximum for an ERP of interest. In our method,
we consider fewer cluster maps for analyzing an optimal number of clusters for
isolating the components of interest in the spatio-temporal ERP. The
statistical comparison demonstrates that the present method outperforms other
conventional approaches. This finding would be practically useful for
discovering the optimal clustering in spatio-temporal ERP, especially when the
cognitive knowledge about time-window is not clearly defined.
| [
{
"created": "Thu, 21 Nov 2019 11:16:10 GMT",
"version": "v1"
}
] | 2019-11-22 | [
[
"Mahini",
"Reza",
""
],
[
"Xu",
"Peng",
""
],
[
"Chen",
"Guoliang",
""
],
[
"Li",
"Yansong",
""
],
[
"Ding",
"Weiyan",
""
],
[
"Zhang",
"Lei",
""
],
[
"Qureshi",
"Nauman Khalid",
""
],
[
"Nandi",
"Asoke K.",
""
],
[
"Cong",
"Fengyu",
""
]
] | Averaging amplitudes over consecutive time samples within a time-window is widely used to calculate the amplitude of an event-related potential (ERP) for cognitive neuroscience. Objective determination of the time-window is critical for determining the ERP component. Clustering on the spatio-temporal ERP data can obtain the time-window in which the consecutive time samples topographies are expected to be highly similar in practice. However, there exists a challenging problem of determining an optimal number of clusters. Here, we develop a novel methodology to obtain the optimal number of clusters using consensus clustering on the spatio-temporal ERP data. Various clustering methods, namely, K-means, hierarchical clustering, fuzzy C-means, self-organizing map, and diffusion maps spectral clustering are combined in an ensemble clustering manner to find the most reliable clusters. When a range of numbers of clusters is applied on the spatio-temporal ERP dataset, the optimal number of clusters should correspond to the cluster of interest within which the average of correlation coefficients between topographies of every two-time sample in the time-window is the maximum for an ERP of interest. In our method, we consider fewer cluster maps for analyzing an optimal number of clusters for isolating the components of interest in the spatio-temporal ERP. The statistical comparison demonstrates that the present method outperforms other conventional approaches. This finding would be practically useful for discovering the optimal clustering in spatio-temporal ERP, especially when the cognitive knowledge about time-window is not clearly defined. |
1509.04090 | Andrew Gainer-Dewar | Elizabeth Drellich, Andrew Gainer-Dewar, Heather A. Harrington, Qijun
He, Christine Heitsch, Svetlana Poznanovi\'c | Geometric combinatorics and computational molecular biology: branching
polytopes for RNA sequences | 17 pages, 8 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Questions in computational molecular biology generate various discrete
optimization problems, such as DNA sequence alignment and RNA secondary
structure prediction. However, the optimal solutions are fundamentally
dependent on the parameters used in the objective functions. The goal of a
parametric analysis is to elucidate such dependencies, especially as they
pertain to the accuracy and robustness of the optimal solutions. Techniques
from geometric combinatorics, including polytopes and their normal fans, have
been used previously to give parametric analyses of simple models for DNA
sequence alignment and RNA branching configurations. Here, we present a new
computational framework, and proof-of-principle results, which give the first
complete parametric analysis of the branching portion of the nearest neighbor
thermodynamic model for secondary structure prediction for real RNA sequences.
| [
{
"created": "Mon, 14 Sep 2015 13:40:44 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Jun 2016 14:30:27 GMT",
"version": "v2"
}
] | 2016-06-17 | [
[
"Drellich",
"Elizabeth",
""
],
[
"Gainer-Dewar",
"Andrew",
""
],
[
"Harrington",
"Heather A.",
""
],
[
"He",
"Qijun",
""
],
[
"Heitsch",
"Christine",
""
],
[
"Poznanović",
"Svetlana",
""
]
] | Questions in computational molecular biology generate various discrete optimization problems, such as DNA sequence alignment and RNA secondary structure prediction. However, the optimal solutions are fundamentally dependent on the parameters used in the objective functions. The goal of a parametric analysis is to elucidate such dependencies, especially as they pertain to the accuracy and robustness of the optimal solutions. Techniques from geometric combinatorics, including polytopes and their normal fans, have been used previously to give parametric analyses of simple models for DNA sequence alignment and RNA branching configurations. Here, we present a new computational framework, and proof-of-principle results, which give the first complete parametric analysis of the branching portion of the nearest neighbor thermodynamic model for secondary structure prediction for real RNA sequences. |
1410.7283 | Xiao Xiao | Xiao Xiao, Kenneth J. Locey, Ethan P. White | A process-independent explanation for the general form of Taylor's Law | 34 pages, 2 table, 3 figures, 2 appendices | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Taylors Law (TL) describes the scaling relationship between the mean and
variance of populations as a power-law. TL is widely observed in ecological
systems across space and time with exponents varying largely between 1 and 2.
Many ecological explanations have been proposed for TL but it is also commonly
observed outside ecology. We propose that TL arises from the constraining
influence of two primary variables: the number of individuals and the number of
censuses or sites. We show that most possible configurations of individuals
among censuses or sites produce the power-law form of TL with exponents between
1 and 2. This feasible set approach suggests that TL is a statistical pattern
driven by two constraints, providing an a priori explanation for this
ubiquitous pattern. However, the exact form of any specific mean-variance
relationship cannot be predicted in this way, i.e., this approach does a poor
job of predicting variation in the exponent, suggesting that TL may still
contain ecological information.
| [
{
"created": "Mon, 27 Oct 2014 15:56:26 GMT",
"version": "v1"
},
{
"created": "Sat, 21 Feb 2015 23:27:25 GMT",
"version": "v2"
}
] | 2015-02-24 | [
[
"Xiao",
"Xiao",
""
],
[
"Locey",
"Kenneth J.",
""
],
[
"White",
"Ethan P.",
""
]
] | Taylors Law (TL) describes the scaling relationship between the mean and variance of populations as a power-law. TL is widely observed in ecological systems across space and time with exponents varying largely between 1 and 2. Many ecological explanations have been proposed for TL but it is also commonly observed outside ecology. We propose that TL arises from the constraining influence of two primary variables: the number of individuals and the number of censuses or sites. We show that most possible configurations of individuals among censuses or sites produce the power-law form of TL with exponents between 1 and 2. This feasible set approach suggests that TL is a statistical pattern driven by two constraints, providing an a priori explanation for this ubiquitous pattern. However, the exact form of any specific mean-variance relationship cannot be predicted in this way, i.e., this approach does a poor job of predicting variation in the exponent, suggesting that TL may still contain ecological information. |
2304.08622 | J. M. Schwarz | Tao Zhang, Sarthak Gupta, Madeline A. Lancaster, J. M. Schwarz | How human-derived brain organoids are built differently from brain
organoids derived of genetically-close relatives: A multi-scale hypothesis | 13 pages, 8 figures | null | null | null | q-bio.TO cond-mat.soft | http://creativecommons.org/licenses/by/4.0/ | How genes affect tissue scale organization remains a longstanding biological
puzzle. As experimental efforts are underway to solve this puzzle via
quantification of gene expression and sub-cellular, cellular and tissue
structure, computational efforts remain far behind. To potentially help
accelerate the computational efforts, we review two recent publications, the
first on a cellular-based model for tissues and the second on a cell nucleus
model consisting of chromatin and a lamina shell. We then give a perspective on
how the two models can be combined to test multiscale hypotheses linking the
chromatin scale and the tissue scale. To be concrete, we turn to an in vitro
system for the brain known as a brain organoid. We provide a multiscale
hypothesis to distinguish structural differences between brain organoids built
from induced-pluripotent human stem cells and from induced-pluripotent gorilla
and chimpanzee stem cells. Recent experiments discover that a cell fate
transition from neuroepithelial cells to radial glial cells includes a new
intermediate state that is delayed in human-derived brain organoids as compared
to their genetically-close relatives, which significantly narrows and lengthens
the cells on the apical side [1]. Additional experiments revealed that the
protein ZEB2 plays a major role in the emergence of this new intermediate state
with ZEB2 mRNA levels peaking at the onset of the emergence [1]. We postulate
that the enhancement of ZEB2 expression driving this intermediate state is
potentially due to chromatin reorganization. More precisely, there exists
critical strain triggering the reorganization that is higher for human-derived
stem cells, thereby resulting in a delay. Such a hypothesis can readily be
tested experimentally within individual cells and within brain organoids as
well as computationally to work towards solving the gene-to-tissue organization
puzzle.
| [
{
"created": "Mon, 17 Apr 2023 21:26:38 GMT",
"version": "v1"
}
] | 2023-04-21 | [
[
"Zhang",
"Tao",
""
],
[
"Gupta",
"Sarthak",
""
],
[
"Lancaster",
"Madeline A.",
""
],
[
"Schwarz",
"J. M.",
""
]
] | How genes affect tissue scale organization remains a longstanding biological puzzle. As experimental efforts are underway to solve this puzzle via quantification of gene expression and sub-cellular, cellular and tissue structure, computational efforts remain far behind. To potentially help accelerate the computational efforts, we review two recent publications, the first on a cellular-based model for tissues and the second on a cell nucleus model consisting of chromatin and a lamina shell. We then give a perspective on how the two models can be combined to test multiscale hypotheses linking the chromatin scale and the tissue scale. To be concrete, we turn to an in vitro system for the brain known as a brain organoid. We provide a multiscale hypothesis to distinguish structural differences between brain organoids built from induced-pluripotent human stem cells and from induced-pluripotent gorilla and chimpanzee stem cells. Recent experiments discover that a cell fate transition from neuroepithelial cells to radial glial cells includes a new intermediate state that is delayed in human-derived brain organoids as compared to their genetically-close relatives, which significantly narrows and lengthens the cells on the apical side [1]. Additional experiments revealed that the protein ZEB2 plays a major role in the emergence of this new intermediate state with ZEB2 mRNA levels peaking at the onset of the emergence [1]. We postulate that the enhancement of ZEB2 expression driving this intermediate state is potentially due to chromatin reorganization. More precisely, there exists critical strain triggering the reorganization that is higher for human-derived stem cells, thereby resulting in a delay. Such a hypothesis can readily be tested experimentally within individual cells and within brain organoids as well as computationally to work towards solving the gene-to-tissue organization puzzle. |
q-bio/0604027 | Giuseppe Gaeta | M. Cadoni, R. De Leo, G. Gaeta | Solitons in a double pendulums chain model, and DNA roto-torsional
dynamics | null | J. Nonlin. Math. Phys. 14 (2007), 128-146 | 10.2991/jnmp.2007.14.1.10 | null | q-bio.BM | null | It was first suggested by Englander et al to model the nonlinear dynamics of
DNA relevant to the transcription process in terms of a chain of coupled
pendulums. In a related paper [q-bio.BM/0604014] we argued for the advantages
of an extension of this approach based on considering a chain of double
pendulums with certain characteristics. Here we study a simplified model of
this kind, focusing on its general features and nonlinear travelling wave
excitations; in particular, we show that some of the degrees of freedom are
actually slaved to others, allowing for an effective reduction of the relevant
equations.
| [
{
"created": "Fri, 21 Apr 2006 12:32:11 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Oct 2006 14:10:50 GMT",
"version": "v2"
}
] | 2015-06-26 | [
[
"Cadoni",
"M.",
""
],
[
"De Leo",
"R.",
""
],
[
"Gaeta",
"G.",
""
]
] | It was first suggested by Englander et al to model the nonlinear dynamics of DNA relevant to the transcription process in terms of a chain of coupled pendulums. In a related paper [q-bio.BM/0604014] we argued for the advantages of an extension of this approach based on considering a chain of double pendulums with certain characteristics. Here we study a simplified model of this kind, focusing on its general features and nonlinear travelling wave excitations; in particular, we show that some of the degrees of freedom are actually slaved to others, allowing for an effective reduction of the relevant equations. |
1903.10859 | Reza Pourimani | Reza Pourimani, Sedigheh Kashian, Ali Asghar Fathivand | Measurement of trace elements in five popular medicinal plants using
Instrumental Neutron Activation Analysis method (INAA) in Arak, Iran | 6 pages, 1 figure, 2 tables | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, the specific mass of twelve elements were determined in five of
the most commonly used medicinal plants as Caraway (Carum carvi), Savory
(Satureia hortensis), Purslane (Portulaca oleracea), Fenugreek (Trigonella
foenum-graecum) and Milk thistle (Silibum marianum) prepared from herbal
pharmacies. Multi elemental Instrumental Neutron Activation Analysis (INAA)
method was applied to analyze the samples. Tehran research reactor was used as
a neutron source and gamma ray spectra registered using high purity germanium
(HPGe) detector. Among analyzed samples, highest concentrations of Fe (8789
ppm), Cr (8 ppm) and Na (517 ppm) were found in Caraway. Maximum levels of Mn
(95 ppm), Cl (3702 ppm), Ca (18328 ppm) , K (21562 ppm) and V (2.7 ppm) were
detected in Savory and Fenugreek contains the lowest concentrations of Fe (195
ppm), Zn (13 ppm), Ca (2243 ppm), Al (99ppm), Mn (26 pm) and Mg (177ppm).
| [
{
"created": "Sat, 5 Jan 2019 12:33:45 GMT",
"version": "v1"
}
] | 2019-03-27 | [
[
"Pourimani",
"Reza",
""
],
[
"Kashian",
"Sedigheh",
""
],
[
"Fathivand",
"Ali Asghar",
""
]
] | In this work, the specific mass of twelve elements were determined in five of the most commonly used medicinal plants as Caraway (Carum carvi), Savory (Satureia hortensis), Purslane (Portulaca oleracea), Fenugreek (Trigonella foenum-graecum) and Milk thistle (Silibum marianum) prepared from herbal pharmacies. Multi elemental Instrumental Neutron Activation Analysis (INAA) method was applied to analyze the samples. Tehran research reactor was used as a neutron source and gamma ray spectra registered using high purity germanium (HPGe) detector. Among analyzed samples, highest concentrations of Fe (8789 ppm), Cr (8 ppm) and Na (517 ppm) were found in Caraway. Maximum levels of Mn (95 ppm), Cl (3702 ppm), Ca (18328 ppm) , K (21562 ppm) and V (2.7 ppm) were detected in Savory and Fenugreek contains the lowest concentrations of Fe (195 ppm), Zn (13 ppm), Ca (2243 ppm), Al (99ppm), Mn (26 pm) and Mg (177ppm). |
1502.07413 | Nao Takashina | Nao Takashina | Simple rules for establishment of effective marine protected areas in an
age-structured metapopulation | 16 pages, 2 figures | Journal of Theoretical Biology, 391:88-94, 2016 | 10.1016/j.jtbi.2015.11.026 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The implementation of effective protected areas is one of the central goals
of modern conservation biology. In the context of fisheries management and
marine ecosystem conservation, marine reserves often play a significant role to
achieve sustainable fisheries management. Consequently, a substantial number of
studies have been conducted to establish broad rules for the creation of MPAs,
or to test the effects of MPAs in specific regions. However, there still exist
many challenges for implementing MPAs that are effective at meeting their
goals. Deducing theoretical conditions guaranteeing that the introduction of
marine reserves will increase fisheries yields in age-structured population
dynamics is one such challenge. To derive such conditions, a simple
mathematical model is developed that follows an age-structured metapopulation
dynamics of a sedentary species. The obtained results suggest that a
sufficiently high fishing mortality rate and moderate recruitment success of an
individual's eggs is a necessary for marine reserves to increase fisheries
yields. The numerical calculations were conducted with the parameters of red
abalone (Haliotis rufescens) to visualize and to check validity of the
analytical results. They show good agreement with the analytical results, as
well as the results obtained in the previous works.
| [
{
"created": "Thu, 26 Feb 2015 01:55:22 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Mar 2015 14:39:44 GMT",
"version": "v2"
},
{
"created": "Fri, 1 Jan 2016 05:23:06 GMT",
"version": "v3"
}
] | 2016-01-05 | [
[
"Takashina",
"Nao",
""
]
] | The implementation of effective protected areas is one of the central goals of modern conservation biology. In the context of fisheries management and marine ecosystem conservation, marine reserves often play a significant role to achieve sustainable fisheries management. Consequently, a substantial number of studies have been conducted to establish broad rules for the creation of MPAs, or to test the effects of MPAs in specific regions. However, there still exist many challenges for implementing MPAs that are effective at meeting their goals. Deducing theoretical conditions guaranteeing that the introduction of marine reserves will increase fisheries yields in age-structured population dynamics is one such challenge. To derive such conditions, a simple mathematical model is developed that follows an age-structured metapopulation dynamics of a sedentary species. The obtained results suggest that a sufficiently high fishing mortality rate and moderate recruitment success of an individual's eggs is a necessary for marine reserves to increase fisheries yields. The numerical calculations were conducted with the parameters of red abalone (Haliotis rufescens) to visualize and to check validity of the analytical results. They show good agreement with the analytical results, as well as the results obtained in the previous works. |
q-bio/0703024 | Vrani Ibarra-Junquera Dr. | P. Escalante-Minakata & V. Ibarra-Junquera | Mixed-Cultures and Alcoholic Fermentations | 10 pages, Language: Spanish | null | null | null | q-bio.PE q-bio.CB | null | The population dynamics of mixed-culture and sequential-cultures in
fruit-must fermentation is a very interesting problem from both the theoretical
and technological stand point. By mixed-culture we refer to those fermentations
in which more that one strain/species are present from the beginning of the
process; and by sequential to those in which different microorganisms are added
along the process. These kinds of fermentations play a key role in industry of
fermented beverages. The mathematical models of such processes should represent
the dynamics of multiple species growing in a mixture of substrates as the
fruit must composition includes an important proportion of hexoses and
pentoses. Since the flavor is a basic aspect of beverages, and in general of
all fermented foods, it is fundamental to study the population dynamics and its
impact in the organoleptic properties, through the influence in the volatile
compound profile. The goal of this paper is to present a panorama of the
investigations of these fermentative ecosystems from a biological,
mathematical, and technological stand point.
| [
{
"created": "Sat, 10 Mar 2007 04:08:06 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Escalante-Minakata",
"P.",
""
],
[
"Ibarra-Junquera",
"V.",
""
]
] | The population dynamics of mixed-culture and sequential-cultures in fruit-must fermentation is a very interesting problem from both the theoretical and technological stand point. By mixed-culture we refer to those fermentations in which more that one strain/species are present from the beginning of the process; and by sequential to those in which different microorganisms are added along the process. These kinds of fermentations play a key role in industry of fermented beverages. The mathematical models of such processes should represent the dynamics of multiple species growing in a mixture of substrates as the fruit must composition includes an important proportion of hexoses and pentoses. Since the flavor is a basic aspect of beverages, and in general of all fermented foods, it is fundamental to study the population dynamics and its impact in the organoleptic properties, through the influence in the volatile compound profile. The goal of this paper is to present a panorama of the investigations of these fermentative ecosystems from a biological, mathematical, and technological stand point. |
2101.05522 | Marco Hartl | M. Hartl (1,2), M.J. Garc\'ia-Gal\'an (1), V. Matamoros (3), M.
Fern\'andez-Gatell (1), D.P.L. Rousseau (2), G. Du Laing (2), M. Garf\'i (1)
and J. Puigagut (1) ((1) GEMMA - Environmental Engineering and Microbiology
Research Group, Department of Civil and Environmental Engineering,
Universitat Polit\`ecnica de Catalunya-BarcelonaTech, Barcelona, Spain, (2)
Department of Green Chemistry and Technology, Faculty of Bioscience
Engineering, Ghent University, Gent, Belgium (3) Department of Environmental
Chemistry, IDAEA-CSIC, Barcelona, Spain) | Constructed wetlands operated as bioelectrochemical systems for the
removal of organic micropollutants | Chemosphere, 38 pages, 1 figure, 4 tables (2 figures and 6 tables in
Supplementary Information) | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The removal of organic micropollutants (OMPs) has been investigated in
constructed wetlands (CWs) operated as bioelectrochemical systems (BES). The
operation of CWs as BES (CW-BES), either in the form of microbial fuel cells
(MFC) or microbial electrolysis cells (MEC), has only been investigated in
recent years. The presented experiment used CW meso-scale systems applying a
realistic horizontal flow regime and continuous feeding of real urban
wastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ),
diclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the
removal efficiency of conventional CW systems (CW-control) as well as CW
systems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although
a few positive trends were identified for the CW-BES compared to the CW-control
(higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC),
these proved to be not statistically significantly different. Mesoscale
experiments with real wastewater could thus not confirm earlier positive
effects of CW-BES found under strictly controlled laboratory conditions with
synthetic wastewaters.
| [
{
"created": "Thu, 14 Jan 2021 09:44:15 GMT",
"version": "v1"
}
] | 2021-01-15 | [
[
"Hartl",
"M.",
""
],
[
"García-Galán",
"M. J.",
""
],
[
"Matamoros",
"V.",
""
],
[
"Fernández-Gatell",
"M.",
""
],
[
"Rousseau",
"D. P. L.",
""
],
[
"Laing",
"G. Du",
""
],
[
"Garfí",
"M.",
""
],
[
"Puigagut",
"J.",
""
]
] | The removal of organic micropollutants (OMPs) has been investigated in constructed wetlands (CWs) operated as bioelectrochemical systems (BES). The operation of CWs as BES (CW-BES), either in the form of microbial fuel cells (MFC) or microbial electrolysis cells (MEC), has only been investigated in recent years. The presented experiment used CW meso-scale systems applying a realistic horizontal flow regime and continuous feeding of real urban wastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ), diclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the removal efficiency of conventional CW systems (CW-control) as well as CW systems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although a few positive trends were identified for the CW-BES compared to the CW-control (higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC), these proved to be not statistically significantly different. Mesoscale experiments with real wastewater could thus not confirm earlier positive effects of CW-BES found under strictly controlled laboratory conditions with synthetic wastewaters. |
1207.5552 | Joseph Pickrell | Joseph K. Pickrell, Nick Patterson, Chiara Barbieri, Falko Berthold,
Linda Gerlach, Tom G\"uldemann, Blesswell Kure, Sununguko Wata Mpoloka,
Hirosi Nakagawa, Christfried Naumann, Mark Lipson, Po-Ru Loh, Joseph
Lachance, Joanna Mountain, Carlos Bustamante, Bonnie Berger, Sarah Tishkoff,
Brenna Henn, Mark Stoneking, David Reich, Brigitte Pakendorf | The genetic prehistory of southern Africa | To appear in Nature Communications | Nat Commun. 2012;3:1143 | 10.1038/ncomms2140 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Southern and eastern African populations that speak non-Bantu languages with
click consonants are known to harbour some of the most ancient genetic lineages
in humans, but their relationships are poorly understood. Here, we report data
from 23 populations analyzed at over half a million single nucleotide
polymorphisms, using a genome-wide array designed for studying human history.
The southern African Khoisan fall into two genetic groups, loosely
corresponding to the northwestern and southeastern Kalahari, which we show
separated within the last 30,000 years. We find that all individuals derive at
least a few percent of their genomes from admixture with non-Khoisan
populations that began approximately 1,200 years ago. In addition, the east
African Hadza and Sandawe derive a fraction of their ancestry from admixture
with a population related to the Khoisan, supporting the hypothesis of an
ancient link between southern and eastern Africa
| [
{
"created": "Mon, 23 Jul 2012 22:35:16 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Sep 2012 18:39:20 GMT",
"version": "v2"
}
] | 2014-04-24 | [
[
"Pickrell",
"Joseph K.",
""
],
[
"Patterson",
"Nick",
""
],
[
"Barbieri",
"Chiara",
""
],
[
"Berthold",
"Falko",
""
],
[
"Gerlach",
"Linda",
""
],
[
"Güldemann",
"Tom",
""
],
[
"Kure",
"Blesswell",
""
],
[
"Mpoloka",
"Sununguko Wata",
""
],
[
"Nakagawa",
"Hirosi",
""
],
[
"Naumann",
"Christfried",
""
],
[
"Lipson",
"Mark",
""
],
[
"Loh",
"Po-Ru",
""
],
[
"Lachance",
"Joseph",
""
],
[
"Mountain",
"Joanna",
""
],
[
"Bustamante",
"Carlos",
""
],
[
"Berger",
"Bonnie",
""
],
[
"Tishkoff",
"Sarah",
""
],
[
"Henn",
"Brenna",
""
],
[
"Stoneking",
"Mark",
""
],
[
"Reich",
"David",
""
],
[
"Pakendorf",
"Brigitte",
""
]
] | Southern and eastern African populations that speak non-Bantu languages with click consonants are known to harbour some of the most ancient genetic lineages in humans, but their relationships are poorly understood. Here, we report data from 23 populations analyzed at over half a million single nucleotide polymorphisms, using a genome-wide array designed for studying human history. The southern African Khoisan fall into two genetic groups, loosely corresponding to the northwestern and southeastern Kalahari, which we show separated within the last 30,000 years. We find that all individuals derive at least a few percent of their genomes from admixture with non-Khoisan populations that began approximately 1,200 years ago. In addition, the east African Hadza and Sandawe derive a fraction of their ancestry from admixture with a population related to the Khoisan, supporting the hypothesis of an ancient link between southern and eastern Africa |
1606.09185 | John Medaglia | John D. Medaglia, Shi Gu, Fabio Pasqualetti, Rebecca L. Ashare, Caryn
Lerman, Joseph Kable, Danielle S. Bassett | Cognitive Control in the Controllable Connectome | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cognition is supported by neurophysiological processes that occur both in
local anatomical neighborhoods and in distributed large-scale circuits. Recent
evidence from network control theory suggests that white matter pathways
linking large-scale brain regions provide a critical substrate constraining the
ability of single areas to affect control on those processes. Yet, no direct
evidence exists for a relationship between brain network controllability and
cognitive control performance. Here, we address this gap by constructing
structural brain networks from diffusion tensor imaging data acquired in 125
healthy adult individuals. We define a simplified model of brain dynamics and
simulate network control to quantify modal and boundary controllability, which
together describe complementary features of a region's theoretically predicted
preference to drive the brain into different cognitive states. We observe that
individual differences in these control features derived from structural
connectivity are significantly correlated with individual differences in
cognitive control performance, as measured by a continuous performance
attention test, a color/shape switching task, the Stroop inhibition task, and a
spatial n-back working memory task. Indeed, control hubs like anterior
cingulate are distinguished from default mode and frontal association areas in
terms of the relationship between their control properties and individual
differences in cognitive function. These results provide the first empirical
evidence that network control forms a fundamental mechanism of cognitive
control.
| [
{
"created": "Wed, 29 Jun 2016 17:07:52 GMT",
"version": "v1"
}
] | 2016-06-30 | [
[
"Medaglia",
"John D.",
""
],
[
"Gu",
"Shi",
""
],
[
"Pasqualetti",
"Fabio",
""
],
[
"Ashare",
"Rebecca L.",
""
],
[
"Lerman",
"Caryn",
""
],
[
"Kable",
"Joseph",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Cognition is supported by neurophysiological processes that occur both in local anatomical neighborhoods and in distributed large-scale circuits. Recent evidence from network control theory suggests that white matter pathways linking large-scale brain regions provide a critical substrate constraining the ability of single areas to affect control on those processes. Yet, no direct evidence exists for a relationship between brain network controllability and cognitive control performance. Here, we address this gap by constructing structural brain networks from diffusion tensor imaging data acquired in 125 healthy adult individuals. We define a simplified model of brain dynamics and simulate network control to quantify modal and boundary controllability, which together describe complementary features of a region's theoretically predicted preference to drive the brain into different cognitive states. We observe that individual differences in these control features derived from structural connectivity are significantly correlated with individual differences in cognitive control performance, as measured by a continuous performance attention test, a color/shape switching task, the Stroop inhibition task, and a spatial n-back working memory task. Indeed, control hubs like anterior cingulate are distinguished from default mode and frontal association areas in terms of the relationship between their control properties and individual differences in cognitive function. These results provide the first empirical evidence that network control forms a fundamental mechanism of cognitive control. |
1511.03721 | Brian Skinner | Brian Skinner | Mathematical toy model inspired by the problem of the adaptive origins
of the sexual orientation continuum | 7 pages, 4 figures; published version | Royal Society Open Science 3: 160403 (2016) | 10.1098/rsos.160403 | null | q-bio.PE cond-mat.stat-mech physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Same-sex sexual behavior is ubiquitous in the animal kingdom, but its
adaptive origins remain a prominent puzzle. Here I suggest the possibility that
same-sex sexual behavior arises as a consequence of the competition between an
evolutionary drive for a wide diversity in traits, which improves the
adaptability of a species, and a drive for sexual dichotomization of traits,
which promotes opposite-sex attraction and increases the rate of reproduction.
A simple analytical "toy model" is proposed for describing this tradeoff. The
model exhibits a number of interesting features, and suggests a simple
mathematical form for describing the sexual orientation continuum.
| [
{
"created": "Wed, 11 Nov 2015 22:54:53 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Sep 2016 18:47:43 GMT",
"version": "v2"
}
] | 2016-09-16 | [
[
"Skinner",
"Brian",
""
]
] | Same-sex sexual behavior is ubiquitous in the animal kingdom, but its adaptive origins remain a prominent puzzle. Here I suggest the possibility that same-sex sexual behavior arises as a consequence of the competition between an evolutionary drive for a wide diversity in traits, which improves the adaptability of a species, and a drive for sexual dichotomization of traits, which promotes opposite-sex attraction and increases the rate of reproduction. A simple analytical "toy model" is proposed for describing this tradeoff. The model exhibits a number of interesting features, and suggests a simple mathematical form for describing the sexual orientation continuum. |
0707.4497 | Patrick De Leenheer | Patrick De Leenheer and Sergei S. Pilyugin | Immune response to a malaria infection: properties of a mathematical
model | null | null | null | null | q-bio.CB | null | We establish some properties of a within host mathematical model of malaria
proposed by Recker et al which includes the role of the immune system during
the infection. The model accounts for the antigenic variation exhibited by the
malaria parasite (P. falciparum). We show that the model can exhibit a wide
variety of dynamical behaviors. We provide criteria for global stability,
competitive exclusion, and persistence. We also demonstrate that the disease
equilibrium can be destabilized by non-symmetric cross-reactive responses.
| [
{
"created": "Mon, 30 Jul 2007 22:49:01 GMT",
"version": "v1"
}
] | 2007-08-01 | [
[
"De Leenheer",
"Patrick",
""
],
[
"Pilyugin",
"Sergei S.",
""
]
] | We establish some properties of a within host mathematical model of malaria proposed by Recker et al which includes the role of the immune system during the infection. The model accounts for the antigenic variation exhibited by the malaria parasite (P. falciparum). We show that the model can exhibit a wide variety of dynamical behaviors. We provide criteria for global stability, competitive exclusion, and persistence. We also demonstrate that the disease equilibrium can be destabilized by non-symmetric cross-reactive responses. |
2204.11006 | Ferenc A. Bartha | Ferenc A. Bartha, N\'ora Juh\'asz, Sadegh Marzban, Renji Han and
Gergely R\"ost | In silico evaluation of Paxlovid's pharmacometrics for SARS-CoV-2: a
multiscale approach | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Paxlovid is a promising, orally bioavailable novel drug for SARS--CoV--2 with
excellent safety profiles. Our main goal here is to explore the pharmacometric
features of this new antiviral. To provide a detailed assessment of Paxlovid,
we propose a hybrid multiscale mathematical approach. We demonstrate that the
results of the present \textit{in silico} evaluation match the clinical
expectations remarkably well: on the one hand, our computations successfully
replicate the outcome of an actual \textit{in vitro} experiment; on the other
hand we verify both the sufficiency and the necessity of Paxlovid's two main
components (nirmatrelvir and ritonavir) for a simplified \textit{in vivo} case.
Moreover, in the simulated context of our computational framework we visualize
the importance of early interventions, and identify the time window where a
unit--length delay causes the highest level of tissue damage. Finally, the
results' sensitivity to the diffusion coefficient of the virus is explored in
details.
| [
{
"created": "Sat, 23 Apr 2022 06:35:30 GMT",
"version": "v1"
}
] | 2022-04-26 | [
[
"Bartha",
"Ferenc A.",
""
],
[
"Juhász",
"Nóra",
""
],
[
"Marzban",
"Sadegh",
""
],
[
"Han",
"Renji",
""
],
[
"Röst",
"Gergely",
""
]
] | Paxlovid is a promising, orally bioavailable novel drug for SARS--CoV--2 with excellent safety profiles. Our main goal here is to explore the pharmacometric features of this new antiviral. To provide a detailed assessment of Paxlovid, we propose a hybrid multiscale mathematical approach. We demonstrate that the results of the present \textit{in silico} evaluation match the clinical expectations remarkably well: on the one hand, our computations successfully replicate the outcome of an actual \textit{in vitro} experiment; on the other hand we verify both the sufficiency and the necessity of Paxlovid's two main components (nirmatrelvir and ritonavir) for a simplified \textit{in vivo} case. Moreover, in the simulated context of our computational framework we visualize the importance of early interventions, and identify the time window where a unit--length delay causes the highest level of tissue damage. Finally, the results' sensitivity to the diffusion coefficient of the virus is explored in details. |
2004.11115 | Fernanda Pinheiro | Jeffrey J. Power, Fernanda Pinheiro, Simone Pompei, Viera Kovacova,
Melih Y\"uksel, Isabel Rathmann, Mona F\"orster, Michael L\"assig, Berenike
Maier | Adaptive evolution of hybrid bacteria by horizontal gene transfer | The first three authors are joint first authors. Corresponding
authors are Lassig and Maier | null | 10.1073/pnas.2007873118 | null | q-bio.PE physics.bio-ph q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Horizontal gene transfer is an important factor in bacterial evolution that
can act across species boundaries. Yet, we know little about rate and genomic
targets of cross-lineage gene transfer, and about its effects on the recipient
organism's physiology and fitness. Here, we address these questions in a
parallel evolution experiment with two Bacillus subtilis lineages of 7%
sequence divergence. We observe rapid evolution of hybrid organisms: gene
transfer swaps ~12% of the core genome in just 200 generations, and 60% of core
genes are replaced in at least one population. By genomics, transcriptomics,
fitness assays, and statistical modeling, we show that transfer generates
adaptive evolution and functional alterations in hybrids. Specifically, our
experiments reveal a strong, repeatable fitness increase of evolved populations
in the stationary growth phase. By genomic analysis of the transfer statistics
across replicate populations, we infer that selection on HGT has a broad
genetic basis: 40% of the observed transfers are adaptive. At the level of
functional gene networks, we find signatures of negative and positive
selection, consistent with hybrid incompatibilities and adaptive evolution of
network functions. Our results suggest that gene transfer navigates a complex
cross-lineage fitness landscape, bridging epistatic barriers along multiple
high-fitness paths.
| [
{
"created": "Thu, 23 Apr 2020 13:00:17 GMT",
"version": "v1"
}
] | 2022-10-12 | [
[
"Power",
"Jeffrey J.",
""
],
[
"Pinheiro",
"Fernanda",
""
],
[
"Pompei",
"Simone",
""
],
[
"Kovacova",
"Viera",
""
],
[
"Yüksel",
"Melih",
""
],
[
"Rathmann",
"Isabel",
""
],
[
"Förster",
"Mona",
""
],
[
"Lässig",
"Michael",
""
],
[
"Maier",
"Berenike",
""
]
] | Horizontal gene transfer is an important factor in bacterial evolution that can act across species boundaries. Yet, we know little about rate and genomic targets of cross-lineage gene transfer, and about its effects on the recipient organism's physiology and fitness. Here, we address these questions in a parallel evolution experiment with two Bacillus subtilis lineages of 7% sequence divergence. We observe rapid evolution of hybrid organisms: gene transfer swaps ~12% of the core genome in just 200 generations, and 60% of core genes are replaced in at least one population. By genomics, transcriptomics, fitness assays, and statistical modeling, we show that transfer generates adaptive evolution and functional alterations in hybrids. Specifically, our experiments reveal a strong, repeatable fitness increase of evolved populations in the stationary growth phase. By genomic analysis of the transfer statistics across replicate populations, we infer that selection on HGT has a broad genetic basis: 40% of the observed transfers are adaptive. At the level of functional gene networks, we find signatures of negative and positive selection, consistent with hybrid incompatibilities and adaptive evolution of network functions. Our results suggest that gene transfer navigates a complex cross-lineage fitness landscape, bridging epistatic barriers along multiple high-fitness paths. |
1306.1372 | Christophe Guyeux | Jacques M. Bahi, Wojciech Bienia, Nathalie C\^ot\'e, Christophe Guyeux | Is protein folding problem really a NP-complete one ? First
investigations | Submitted to Journal of Bioinformatics and Computational Biology,
under review | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To determine the 3D conformation of proteins is a necessity to understand
their functions or interactions with other molecules. It is commonly admitted
that, when proteins fold from their primary linear structures to their final 3D
conformations, they tend to choose the ones that minimize their free energy. To
find the 3D conformation of a protein knowing its amino acid sequence,
bioinformaticians use various models of different resolutions and artificial
intelligence tools, as the protein folding prediction problem is a NP complete
one. More precisely, to determine the backbone structure of the protein using
the low resolution models (2D HP square and 3D HP cubic), by finding the
conformation that minimize free energy, is intractable exactly. Both the proof
of NP-completeness and the 2D prediction consider that acceptable conformations
have to satisfy a self-avoiding walk (SAW) requirement, as two different amino
acids cannot occupy a same position in the lattice. It is shown in this
document that the SAW requirement considered when proving NP-completeness is
different from the SAW requirement used in various prediction programs, and
that they are different from the real biological requirement. Indeed, the proof
of NP completeness and the predictions in silico consider conformations that
are not possible in practice. Consequences of this fact are investigated in
this research work.
| [
{
"created": "Thu, 6 Jun 2013 11:09:18 GMT",
"version": "v1"
}
] | 2013-06-07 | [
[
"Bahi",
"Jacques M.",
""
],
[
"Bienia",
"Wojciech",
""
],
[
"Côté",
"Nathalie",
""
],
[
"Guyeux",
"Christophe",
""
]
] | To determine the 3D conformation of proteins is a necessity to understand their functions or interactions with other molecules. It is commonly admitted that, when proteins fold from their primary linear structures to their final 3D conformations, they tend to choose the ones that minimize their free energy. To find the 3D conformation of a protein knowing its amino acid sequence, bioinformaticians use various models of different resolutions and artificial intelligence tools, as the protein folding prediction problem is a NP complete one. More precisely, to determine the backbone structure of the protein using the low resolution models (2D HP square and 3D HP cubic), by finding the conformation that minimize free energy, is intractable exactly. Both the proof of NP-completeness and the 2D prediction consider that acceptable conformations have to satisfy a self-avoiding walk (SAW) requirement, as two different amino acids cannot occupy a same position in the lattice. It is shown in this document that the SAW requirement considered when proving NP-completeness is different from the SAW requirement used in various prediction programs, and that they are different from the real biological requirement. Indeed, the proof of NP completeness and the predictions in silico consider conformations that are not possible in practice. Consequences of this fact are investigated in this research work. |
1301.6027 | Duncan Blythe | Duncan A.J. Blythe, Frank C. Meinecke, Paul von Buenau and
Klaus-Robert Mueller | Explorative Data Analysis for Changes in Neural Activity | null | null | null | null | q-bio.QM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural recordings are nonstationary time series, i.e. their properties
typically change over time. Identifying specific changes, e.g. those induced by
a learning task, can shed light on the underlying neural processes. However,
such changes of interest are often masked by strong unrelated changes, which
can be of physiological origin or due to measurement artifacts. We propose a
novel algorithm for disentangling such different causes of non-stationarity and
in this manner enable better neurophysiological interpretation for a wider set
of experimental paradigms. A key ingredient is the repeated application of
Stationary Subspace Analysis (SSA) using different temporal scales. The
usefulness of our explorative approach is demonstrated in simulations, theory
and EEG experiments with 80 Brain-Computer-Interfacing (BCI) subjects.
| [
{
"created": "Fri, 25 Jan 2013 12:26:41 GMT",
"version": "v1"
}
] | 2013-01-28 | [
[
"Blythe",
"Duncan A. J.",
""
],
[
"Meinecke",
"Frank C.",
""
],
[
"von Buenau",
"Paul",
""
],
[
"Mueller",
"Klaus-Robert",
""
]
] | Neural recordings are nonstationary time series, i.e. their properties typically change over time. Identifying specific changes, e.g. those induced by a learning task, can shed light on the underlying neural processes. However, such changes of interest are often masked by strong unrelated changes, which can be of physiological origin or due to measurement artifacts. We propose a novel algorithm for disentangling such different causes of non-stationarity and in this manner enable better neurophysiological interpretation for a wider set of experimental paradigms. A key ingredient is the repeated application of Stationary Subspace Analysis (SSA) using different temporal scales. The usefulness of our explorative approach is demonstrated in simulations, theory and EEG experiments with 80 Brain-Computer-Interfacing (BCI) subjects. |
q-bio/0611039 | Dmitry Kondrashov | Dmitry A. Kondrashov, Adam W. Van Wynsberghe, Ryan M. Bannen, Qiang
Cui, and George N. Phillips Jr | Protein structural variation in computational models and
crystallographic data | 17 pages, 4 figures | null | null | null | q-bio.BM q-bio.QM | null | Normal mode analysis offers an efficient way of modeling the conformational
flexibility of protein structures. Simple models defined by contact topology,
known as elastic network models, have been used to model a variety of systems,
but the validation is typically limited to individual modes for a single
protein. We use anisotropic displacement parameters from crystallography to
test the quality of prediction of both the magnitude and directionality of
conformational variance. Normal modes from four simple elastic network model
potentials and from the CHARMM forcefield are calculated for a data set of 83
diverse, ultrahigh resolution crystal structures. While all five potentials
provide good predictions of the magnitude of flexibility, the methods that
consider all atoms have a clear edge at prediction of directionality, and the
CHARMM potential produces the best agreement. The low-frequency modes from
different potentials are similar, but those computed from the CHARMM potential
show the greatest difference from the elastic network models. This was
illustrated by computing the dynamic correlation matrices from different
potentials for a PDZ domain structure. Comparison of normal mode results with
anisotropic temperature factors opens the possibility of using ultrahigh
resolution crystallographic data as a quantitative measure of molecular
flexibility. The comprehensive evaluation demonstrates the costs and benefits
of using normal mode potentials of varying complexity. Comparison of the
dynamic correlation matrices suggests that a combination of topological and
chemical potentials may help identify residues in which chemical forces make
large contributions to intramolecular coupling.
| [
{
"created": "Thu, 9 Nov 2006 16:36:03 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Kondrashov",
"Dmitry A.",
""
],
[
"Van Wynsberghe",
"Adam W.",
""
],
[
"Bannen",
"Ryan M.",
""
],
[
"Cui",
"Qiang",
""
],
[
"Phillips",
"George N.",
"Jr"
]
] | Normal mode analysis offers an efficient way of modeling the conformational flexibility of protein structures. Simple models defined by contact topology, known as elastic network models, have been used to model a variety of systems, but the validation is typically limited to individual modes for a single protein. We use anisotropic displacement parameters from crystallography to test the quality of prediction of both the magnitude and directionality of conformational variance. Normal modes from four simple elastic network model potentials and from the CHARMM forcefield are calculated for a data set of 83 diverse, ultrahigh resolution crystal structures. While all five potentials provide good predictions of the magnitude of flexibility, the methods that consider all atoms have a clear edge at prediction of directionality, and the CHARMM potential produces the best agreement. The low-frequency modes from different potentials are similar, but those computed from the CHARMM potential show the greatest difference from the elastic network models. This was illustrated by computing the dynamic correlation matrices from different potentials for a PDZ domain structure. Comparison of normal mode results with anisotropic temperature factors opens the possibility of using ultrahigh resolution crystallographic data as a quantitative measure of molecular flexibility. The comprehensive evaluation demonstrates the costs and benefits of using normal mode potentials of varying complexity. Comparison of the dynamic correlation matrices suggests that a combination of topological and chemical potentials may help identify residues in which chemical forces make large contributions to intramolecular coupling. |
1205.3532 | Soheil Jahangiri Tazehkand | Soheil Jahangiri Tazehkand, Seyed Naser Hashemi, Hadi Poormohammadi | New Algorithms on Rooted Triplet Consistency | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An evolutionary tree (phylogenetic tree) is a binary, rooted, unordered tree
that models the evolutionary history of currently living species in which
leaves are labeled by species. In this paper, we investigate the problem of
finding the maximum consensus evolutionary tree from a set of given rooted
triplets. A rooted triplet is a phylogenetic tree on three leaves and shows the
evolutionary relationship of the corresponding three species. The mentioned
problem is known to be APX-hard. We present two new heuristic algorithms. For a
given set of m triplets on n species, the FastTree algorithm runs in O(mn^2)
which is faster than any other previously known algorithms, although, the
outcome is less satisfactory. The BPMTR algorithm runs in O(mn^3) and in
average performs better than any other previously known approximation
algorithms for this problem.
| [
{
"created": "Wed, 16 May 2012 00:25:31 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Jul 2012 10:19:42 GMT",
"version": "v2"
},
{
"created": "Mon, 1 Apr 2013 17:23:24 GMT",
"version": "v3"
}
] | 2013-04-02 | [
[
"Tazehkand",
"Soheil Jahangiri",
""
],
[
"Hashemi",
"Seyed Naser",
""
],
[
"Poormohammadi",
"Hadi",
""
]
] | An evolutionary tree (phylogenetic tree) is a binary, rooted, unordered tree that models the evolutionary history of currently living species in which leaves are labeled by species. In this paper, we investigate the problem of finding the maximum consensus evolutionary tree from a set of given rooted triplets. A rooted triplet is a phylogenetic tree on three leaves and shows the evolutionary relationship of the corresponding three species. The mentioned problem is known to be APX-hard. We present two new heuristic algorithms. For a given set of m triplets on n species, the FastTree algorithm runs in O(mn^2) which is faster than any other previously known algorithms, although, the outcome is less satisfactory. The BPMTR algorithm runs in O(mn^3) and in average performs better than any other previously known approximation algorithms for this problem. |
2403.12788 | Markus D Schirmer | Kenda Alhadid, Robert W. Regenhardt, Natalia S. Rost, Markus D.
Schirmer | Brain volume is a better biomarker of outcomes in ischemic stroke
compared to brain atrophy | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brain parenchymal fraction (BPF) has been used as a surrogate measure of
global brain atrophy, and as a biomarker of brain reserve in studies evaluating
clinical outcomes after brain injury. Total brain volume at the time of injury
has recently been shown to influence functional outcomes, where larger brain
volumes are associated with better outcomes. Here, we assess if brain volume at
the time of ischemic stroke injury is a better biomarker of functional outcome
than BPF. Acute ischemic stroke cases at a single center between 2003 and 2011,
with MR neuroimaging obtained within 48 hours from presentation were eligible.
Functional outcomes represented by the modified Rankin Score (mRS) at 90 days
post admission (mRS<3 deemed a favorable outcome) were obtained via patient
interview or per chart review. Deep learning enabled automated segmentation
pipelines were used to calculate brain volume, intracranial volume (ICV), and
BPF on the acute neuroimaging data. Patient outcomes were modeled through
logistic regressions, and model comparison was conducted using the Bayes
Information Criterion (BIC). 467 patients with arterial ischemic stroke were
included in the analysis. Median age was 65.8 years, and 65.3% were male. In
both models, age and a larger stroke lesion volume were associated with worse
functional outcomes. Higher BPF and a larger brain volume were both associated
with favorable functional outcomes, however, comparison of both models
suggested that the brain volume model (BIC=501) explains the data better
compared to the BPF model (BIC=511). The extent of global brain atrophy has
been regarded as an important biomarker of post-stroke functional outcomes and
resilience to acute injury. Here, we demonstrate that a higher global brain
volume at the time of injury better explains favorable functional outcomes,
which can be directly clinically assessed.
| [
{
"created": "Tue, 19 Mar 2024 14:51:54 GMT",
"version": "v1"
}
] | 2024-03-20 | [
[
"Alhadid",
"Kenda",
""
],
[
"Regenhardt",
"Robert W.",
""
],
[
"Rost",
"Natalia S.",
""
],
[
"Schirmer",
"Markus D.",
""
]
] | Brain parenchymal fraction (BPF) has been used as a surrogate measure of global brain atrophy, and as a biomarker of brain reserve in studies evaluating clinical outcomes after brain injury. Total brain volume at the time of injury has recently been shown to influence functional outcomes, where larger brain volumes are associated with better outcomes. Here, we assess if brain volume at the time of ischemic stroke injury is a better biomarker of functional outcome than BPF. Acute ischemic stroke cases at a single center between 2003 and 2011, with MR neuroimaging obtained within 48 hours from presentation were eligible. Functional outcomes represented by the modified Rankin Score (mRS) at 90 days post admission (mRS<3 deemed a favorable outcome) were obtained via patient interview or per chart review. Deep learning enabled automated segmentation pipelines were used to calculate brain volume, intracranial volume (ICV), and BPF on the acute neuroimaging data. Patient outcomes were modeled through logistic regressions, and model comparison was conducted using the Bayes Information Criterion (BIC). 467 patients with arterial ischemic stroke were included in the analysis. Median age was 65.8 years, and 65.3% were male. In both models, age and a larger stroke lesion volume were associated with worse functional outcomes. Higher BPF and a larger brain volume were both associated with favorable functional outcomes, however, comparison of both models suggested that the brain volume model (BIC=501) explains the data better compared to the BPF model (BIC=511). The extent of global brain atrophy has been regarded as an important biomarker of post-stroke functional outcomes and resilience to acute injury. Here, we demonstrate that a higher global brain volume at the time of injury better explains favorable functional outcomes, which can be directly clinically assessed. |
2208.05482 | Jie Wang | Huarui He, Jie Wang, Yunfei Liu, Feng Wu | Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via
Discrete Latent Variables | Accepted to CIKM 2022 | null | null | null | q-bio.QM cs.LG q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single-step retrosynthesis is the cornerstone of retrosynthesis planning,
which is a crucial task for computer-aided drug discovery. The goal of
single-step retrosynthesis is to identify the possible reactants that lead to
the synthesis of the target product in one reaction. By representing organic
molecules as canonical strings, existing sequence-based retrosynthetic methods
treat the product-to-reactant retrosynthesis as a sequence-to-sequence
translation problem. However, most of them struggle to identify diverse
chemical reactions for a desired product due to the deterministic inference,
which contradicts the fact that many compounds can be synthesized through
various reaction types with different sets of reactants. In this work, we aim
to increase reaction diversity and generate various reactants using discrete
latent variables. We propose a novel sequence-based approach, namely
RetroDVCAE, which incorporates conditional variational autoencoders into
single-step retrosynthesis and associates discrete latent variables with the
generation process. Specifically, RetroDVCAE uses the Gumbel-Softmax
distribution to approximate the categorical distribution over potential
reactions and generates multiple sets of reactants with the variational
decoder. Experiments demonstrate that RetroDVCAE outperforms state-of-the-art
baselines on both benchmark dataset and homemade dataset. Both quantitative and
qualitative results show that RetroDVCAE can model the multi-modal distribution
over reaction types and produce diverse reactant candidates.
| [
{
"created": "Wed, 10 Aug 2022 14:50:32 GMT",
"version": "v1"
}
] | 2022-08-12 | [
[
"He",
"Huarui",
""
],
[
"Wang",
"Jie",
""
],
[
"Liu",
"Yunfei",
""
],
[
"Wu",
"Feng",
""
]
] | Single-step retrosynthesis is the cornerstone of retrosynthesis planning, which is a crucial task for computer-aided drug discovery. The goal of single-step retrosynthesis is to identify the possible reactants that lead to the synthesis of the target product in one reaction. By representing organic molecules as canonical strings, existing sequence-based retrosynthetic methods treat the product-to-reactant retrosynthesis as a sequence-to-sequence translation problem. However, most of them struggle to identify diverse chemical reactions for a desired product due to the deterministic inference, which contradicts the fact that many compounds can be synthesized through various reaction types with different sets of reactants. In this work, we aim to increase reaction diversity and generate various reactants using discrete latent variables. We propose a novel sequence-based approach, namely RetroDVCAE, which incorporates conditional variational autoencoders into single-step retrosynthesis and associates discrete latent variables with the generation process. Specifically, RetroDVCAE uses the Gumbel-Softmax distribution to approximate the categorical distribution over potential reactions and generates multiple sets of reactants with the variational decoder. Experiments demonstrate that RetroDVCAE outperforms state-of-the-art baselines on both benchmark dataset and homemade dataset. Both quantitative and qualitative results show that RetroDVCAE can model the multi-modal distribution over reaction types and produce diverse reactant candidates. |
q-bio/0406022 | Mandar Inamdar | Prashant K. Purohit, Mandar M. Inamdar, Paul D. Grayson, Todd M.
Squires, Jane' Kondev, Rob Phillips | Forces During Bacteriophage DNA Packaging and Ejection | null | null | 10.1529/biophysj.104.047134 | null | q-bio.BM | null | The conjunction of insights from structural biology, solution biochemistry,
genetics and single molecule biophysics has provided a renewed impetus for the
construction of quantitative models of biological processes. One area that has
been a beneficiary of these experimental techniques is the study of viruses. In
this paper we describe how the insights obtained from such experiments can be
utilized to construct physical models of processes in the viral life cycle. We
focus on dsDNA bacteriophages and show that the bending elasticity of DNA and
its electrostatics in solution can be combined to determine the forces
experienced during packaging and ejection of the viral genome. Furthermore, we
quantitatively analyze the effect of fluid viscosity and capsid expansion on
the forces experienced during packaging. Finally, we present a model for DNA
ejection from bacteriophages based on the hypothesis that the energy stored in
the tightly packed genome within the capsid leads to its forceful ejection. The
predictions of our model can be tested through experiments in vitro where DNA
ejection is inhibited by the application of external osmotic pressure.
| [
{
"created": "Thu, 10 Jun 2004 00:32:25 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Purohit",
"Prashant K.",
""
],
[
"Inamdar",
"Mandar M.",
""
],
[
"Grayson",
"Paul D.",
""
],
[
"Squires",
"Todd M.",
""
],
[
"Kondev",
"Jane'",
""
],
[
"Phillips",
"Rob",
""
]
] | The conjunction of insights from structural biology, solution biochemistry, genetics and single molecule biophysics has provided a renewed impetus for the construction of quantitative models of biological processes. One area that has been a beneficiary of these experimental techniques is the study of viruses. In this paper we describe how the insights obtained from such experiments can be utilized to construct physical models of processes in the viral life cycle. We focus on dsDNA bacteriophages and show that the bending elasticity of DNA and its electrostatics in solution can be combined to determine the forces experienced during packaging and ejection of the viral genome. Furthermore, we quantitatively analyze the effect of fluid viscosity and capsid expansion on the forces experienced during packaging. Finally, we present a model for DNA ejection from bacteriophages based on the hypothesis that the energy stored in the tightly packed genome within the capsid leads to its forceful ejection. The predictions of our model can be tested through experiments in vitro where DNA ejection is inhibited by the application of external osmotic pressure. |
2005.09630 | Magdalena Djordjevic | Magdalena Djordjevic, Andjela Rodic, Igor Salom, Dusan Zigic, Ognjen
Milicevic, Bojana Ilic, Marko Djordjevic | A systems biology approach to COVID-19 progression in a population | 16 pages, 5 figures, 1 table. Advances in Protein Chemistry and
Structural Biology (in press, 2021) | Adv Protein Chem Struct Biol . 2021;127:291-314 | 10.1016/bs.apcsb.2021.03.003 | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A number of models in mathematical epidemiology have been developed to
account for control measures such as vaccination or quarantine. However,
COVID-19 has brought unprecedented social distancing measures, with a challenge
on how to include these in a manner that can explain the data but avoid
overfitting in parameter inference. We here develop a simple time-dependent
model, where social distancing effects are introduced analogous to
coarse-grained models of gene expression control in systems biology. We apply
our approach to understand drastic differences in COVID-19 infection and
fatality counts, observed between Hubei (Wuhan) and other Mainland China
provinces. We find that these unintuitive data may be explained through an
interplay of differences in transmissibility, effective protection, and
detection efficiencies between Hubei and other provinces. More generally, our
results demonstrate that regional differences may drastically shape infection
outbursts. The obtained results demonstrate the applicability of our developed
method to extract key infection parameters directly from publically available
data so that it can be globally applied to outbreaks of COVID-19 in a number of
countries. Overall, we show that applications of uncommon strategies, such as
methods and approaches from molecular systems biology research to mathematical
epidemiology, may significantly advance our understanding of COVID-19 and other
infectious diseases.
| [
{
"created": "Tue, 19 May 2020 17:57:41 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Jun 2020 19:52:43 GMT",
"version": "v2"
},
{
"created": "Sun, 28 Mar 2021 11:35:15 GMT",
"version": "v3"
}
] | 2021-09-07 | [
[
"Djordjevic",
"Magdalena",
""
],
[
"Rodic",
"Andjela",
""
],
[
"Salom",
"Igor",
""
],
[
"Zigic",
"Dusan",
""
],
[
"Milicevic",
"Ognjen",
""
],
[
"Ilic",
"Bojana",
""
],
[
"Djordjevic",
"Marko",
""
]
] | A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases. |
2404.19230 | Haotian Zhang | Odin Zhang, Haitao Lin, Hui Zhang, Huifeng Zhao, Yufei Huang,
Yuansheng Huang, Dejun Jiang, Chang-yu Hsieh, Peichen Pan, Tingjun Hou | Deep Lead Optimization: Leveraging Generative AI for Structural
Modification | null | null | null | null | q-bio.BM cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The idea of using deep-learning-based molecular generation to accelerate
discovery of drug candidates has attracted extraordinary attention, and many
deep generative models have been developed for automated drug design, termed
molecular generation. In general, molecular generation encompasses two main
strategies: de novo design, which generates novel molecular structures from
scratch, and lead optimization, which refines existing molecules into drug
candidates. Among them, lead optimization plays an important role in real-world
drug design. For example, it can enable the development of me-better drugs that
are chemically distinct yet more effective than the original drugs. It can also
facilitate fragment-based drug design, transforming virtual-screened small
ligands with low affinity into first-in-class medicines. Despite its
importance, automated lead optimization remains underexplored compared to the
well-established de novo generative models, due to its reliance on complex
biological and chemical knowledge. To bridge this gap, we conduct a systematic
review of traditional computational methods for lead optimization, organizing
these strategies into four principal sub-tasks with defined inputs and outputs.
This review delves into the basic concepts, goals, conventional CADD
techniques, and recent advancements in AIDD. Additionally, we introduce a
unified perspective based on constrained subgraph generation to harmonize the
methodologies of de novo design and lead optimization. Through this lens, de
novo design can incorporate strategies from lead optimization to address the
challenge of generating hard-to-synthesize molecules; inversely, lead
optimization can benefit from the innovations in de novo design by approaching
it as a task of generating molecules conditioned on certain substructures.
| [
{
"created": "Tue, 30 Apr 2024 03:17:42 GMT",
"version": "v1"
}
] | 2024-05-01 | [
[
"Zhang",
"Odin",
""
],
[
"Lin",
"Haitao",
""
],
[
"Zhang",
"Hui",
""
],
[
"Zhao",
"Huifeng",
""
],
[
"Huang",
"Yufei",
""
],
[
"Huang",
"Yuansheng",
""
],
[
"Jiang",
"Dejun",
""
],
[
"Hsieh",
"Chang-yu",
""
],
[
"Pan",
"Peichen",
""
],
[
"Hou",
"Tingjun",
""
]
] | The idea of using deep-learning-based molecular generation to accelerate discovery of drug candidates has attracted extraordinary attention, and many deep generative models have been developed for automated drug design, termed molecular generation. In general, molecular generation encompasses two main strategies: de novo design, which generates novel molecular structures from scratch, and lead optimization, which refines existing molecules into drug candidates. Among them, lead optimization plays an important role in real-world drug design. For example, it can enable the development of me-better drugs that are chemically distinct yet more effective than the original drugs. It can also facilitate fragment-based drug design, transforming virtual-screened small ligands with low affinity into first-in-class medicines. Despite its importance, automated lead optimization remains underexplored compared to the well-established de novo generative models, due to its reliance on complex biological and chemical knowledge. To bridge this gap, we conduct a systematic review of traditional computational methods for lead optimization, organizing these strategies into four principal sub-tasks with defined inputs and outputs. This review delves into the basic concepts, goals, conventional CADD techniques, and recent advancements in AIDD. Additionally, we introduce a unified perspective based on constrained subgraph generation to harmonize the methodologies of de novo design and lead optimization. Through this lens, de novo design can incorporate strategies from lead optimization to address the challenge of generating hard-to-synthesize molecules; inversely, lead optimization can benefit from the innovations in de novo design by approaching it as a task of generating molecules conditioned on certain substructures. |
2305.09317 | Parvin Zarei Eskikand | Parvin Zarei Eskikand, David B Grayden, Tatiana Kameneva, Anthony N
Burkitt, Michael R Ibbotson | Understanding visual processing of motion: Completing the picture using
experimentally driven computational models of MT | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Computational modeling helps neuroscientists to integrate and explain
experimental data obtained through neurophysiological and anatomical studies,
thus providing a mechanism by which we can better understand and predict the
principles of neural computation. Computational modeling of the neuronal
pathways of the visual cortex has been successful in developing theories of
biological motion processing. This review describes a range of computational
models that have been inspired by neurophysiological experiments. Theories of
local motion integration and pattern motion processing are presented, together
with suggested neurophysiological experiments designed to test those
hypotheses.
| [
{
"created": "Tue, 16 May 2023 09:47:02 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Sep 2023 04:21:40 GMT",
"version": "v2"
}
] | 2023-09-22 | [
[
"Eskikand",
"Parvin Zarei",
""
],
[
"Grayden",
"David B",
""
],
[
"Kameneva",
"Tatiana",
""
],
[
"Burkitt",
"Anthony N",
""
],
[
"Ibbotson",
"Michael R",
""
]
] | Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses. |
1511.07962 | Christoph Adami | Jory Schossau, Christoph Adami, and Arend Hintze | Information-theoretic neuro-correlates boost evolution of cognitive
systems | 26 pages, 6 figures plus 3 Suppl. figures (included). To appear in
special issue "Information Theoretic Incentives for Cognitive Systems" of
journal "Entropy" | Entropy 18 (2016) 6 | 10.3390/e18010006 | null | q-bio.NC cs.IT math.IT nlin.AO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic Algorithms (GA) are a powerful set of tools for search and
optimization that mimic the process of natural selection, and have been used
successfully in a wide variety of problems, including evolving neural networks
to solve cognitive tasks. Despite their success, GAs sometimes fail to locate
the highest peaks of the fitness landscape, in particular if the landscape is
rugged and contains multiple peaks. Reaching distant and higher peaks is
difficult because valleys need to be crossed, in a process that (at least
temporarily) runs against the fitness maximization objective. Here we propose
and test a number of information-theoretic (as well as network-based) measures
that can be used in conjunction with a fitness maximization objective
(so-called ``neuro-correlates") to evolve neural controllers for two widely
different tasks: a behavioral task that requires information integration, and a
cognitive task that requires memory and logic. We find that judiciously chosen
neuro-correlates can significantly aid GAs to find the highest peaks.
| [
{
"created": "Wed, 25 Nov 2015 06:07:10 GMT",
"version": "v1"
}
] | 2016-01-05 | [
[
"Schossau",
"Jory",
""
],
[
"Adami",
"Christoph",
""
],
[
"Hintze",
"Arend",
""
]
] | Genetic Algorithms (GA) are a powerful set of tools for search and optimization that mimic the process of natural selection, and have been used successfully in a wide variety of problems, including evolving neural networks to solve cognitive tasks. Despite their success, GAs sometimes fail to locate the highest peaks of the fitness landscape, in particular if the landscape is rugged and contains multiple peaks. Reaching distant and higher peaks is difficult because valleys need to be crossed, in a process that (at least temporarily) runs against the fitness maximization objective. Here we propose and test a number of information-theoretic (as well as network-based) measures that can be used in conjunction with a fitness maximization objective (so-called ``neuro-correlates") to evolve neural controllers for two widely different tasks: a behavioral task that requires information integration, and a cognitive task that requires memory and logic. We find that judiciously chosen neuro-correlates can significantly aid GAs to find the highest peaks. |
1402.0757 | Philip Gerlee | Philip Gerlee, Alexander R.A. Anderson | The evolution of carrying capacity in constrained and expanding tumour
cell populations | Major revisions compared to previous version. The paper is now aimed
at tumour modelling. We now start out with an agent-based model for which we
derive a mean-field ODE-model. The ODE-model is further analysed using the
theory of adaptive dynamics | null | 10.1088/1478-3975/12/5/056001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cancer cells are known to modify their micro-environment such that it can
sustain a larger population, or, in ecological terms, they construct a niche
which increases the carrying capacity of the population. It has however been
argued that niche construction, which benefits all cells in the tumour, would
be selected against since cheaters could reap the benefits without paying the
cost. We have investigated the impact of niche specificity on tumour evolution
using an individual based model of breast tumour growth, in which the carrying
capacity of each cell consists of two components: an intrinsic,
subclone-specific part and a contribution from all neighbouring cells. Analysis
of the model shows that the ability of a mutant to invade a resident population
depends strongly on the specificity. When specificity is low selection is
mostly on growth rate, while high specificity shifts selection towards
increased carrying capacity. Further, we show that the long-term evolution of
the system can be predicted using adaptive dynamics. By comparing the results
from a spatially structured vs.\ well-mixed population we show that spatial
structure restores selection for carrying capacity even at zero specificity,
which a poses solution to the niche construction dilemma. Lastly, we show that
an expanding population exhibits spatially variable selection pressure, where
cells at the leading edge exhibit higher growth rate and lower carrying
capacity than those at the centre of the tumour.
| [
{
"created": "Tue, 4 Feb 2014 15:12:04 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Aug 2015 08:16:56 GMT",
"version": "v2"
}
] | 2015-09-02 | [
[
"Gerlee",
"Philip",
""
],
[
"Anderson",
"Alexander R. A.",
""
]
] | Cancer cells are known to modify their micro-environment such that it can sustain a larger population, or, in ecological terms, they construct a niche which increases the carrying capacity of the population. It has however been argued that niche construction, which benefits all cells in the tumour, would be selected against since cheaters could reap the benefits without paying the cost. We have investigated the impact of niche specificity on tumour evolution using an individual based model of breast tumour growth, in which the carrying capacity of each cell consists of two components: an intrinsic, subclone-specific part and a contribution from all neighbouring cells. Analysis of the model shows that the ability of a mutant to invade a resident population depends strongly on the specificity. When specificity is low selection is mostly on growth rate, while high specificity shifts selection towards increased carrying capacity. Further, we show that the long-term evolution of the system can be predicted using adaptive dynamics. By comparing the results from a spatially structured vs.\ well-mixed population we show that spatial structure restores selection for carrying capacity even at zero specificity, which a poses solution to the niche construction dilemma. Lastly, we show that an expanding population exhibits spatially variable selection pressure, where cells at the leading edge exhibit higher growth rate and lower carrying capacity than those at the centre of the tumour. |
1703.05951 | Viktor Stojkoski MSc | Zoran Utkovski, Viktor Stojkoski, Lasko Basnarkov and Ljupco Kocarev | Promoting cooperation by preventing exploitation: The role of network
structure | 11 pages, 5 figures | Phys. Rev. E 96, 022315 (2017) | 10.1103/PhysRevE.96.022315 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A growing body of empirical evidence indicates that social and cooperative
behavior can be affected by cognitive and neurological factors, suggesting the
existence of state-based decision-making mechanisms that may have emerged by
evolution. Motivated by these observations, we propose a simple mechanism of
anonymous network interactions identified as a form of generalized reciprocity
- a concept organized around the premise "help anyone if helped by someone",
and study its dynamics on random graphs. In the presence of such mechanism, the
evolution of cooperation is related to the dynamics of the levels of
investments (i.e. probabilities of cooperation) of the individual nodes
engaging in interactions. We demonstrate that the propensity for cooperation is
determined by a network centrality measure here referred to as neighborhood
importance index and discuss relevant implications to natural and artificial
systems. To address the robustness of the state-based strategies to an invasion
of defectors, we additionally provide an analysis which redefines the results
for the case when a fraction of the nodes behave as unconditional defectors.
| [
{
"created": "Fri, 17 Mar 2017 10:24:04 GMT",
"version": "v1"
},
{
"created": "Thu, 25 May 2017 12:03:58 GMT",
"version": "v2"
},
{
"created": "Tue, 1 Aug 2017 12:13:14 GMT",
"version": "v3"
},
{
"created": "Wed, 2 Aug 2017 15:15:36 GMT",
"version": "v4"
}
] | 2017-08-23 | [
[
"Utkovski",
"Zoran",
""
],
[
"Stojkoski",
"Viktor",
""
],
[
"Basnarkov",
"Lasko",
""
],
[
"Kocarev",
"Ljupco",
""
]
] | A growing body of empirical evidence indicates that social and cooperative behavior can be affected by cognitive and neurological factors, suggesting the existence of state-based decision-making mechanisms that may have emerged by evolution. Motivated by these observations, we propose a simple mechanism of anonymous network interactions identified as a form of generalized reciprocity - a concept organized around the premise "help anyone if helped by someone", and study its dynamics on random graphs. In the presence of such mechanism, the evolution of cooperation is related to the dynamics of the levels of investments (i.e. probabilities of cooperation) of the individual nodes engaging in interactions. We demonstrate that the propensity for cooperation is determined by a network centrality measure here referred to as neighborhood importance index and discuss relevant implications to natural and artificial systems. To address the robustness of the state-based strategies to an invasion of defectors, we additionally provide an analysis which redefines the results for the case when a fraction of the nodes behave as unconditional defectors. |
1510.04230 | Jose Davila-Velderrain | Jose Davila-Velderrain, Luis Juarez-Ramiro, Juan C. Martinez-Garcia,
Elena R. Alvarez-Buylla | Methods for Characterizing the Epigenetic Attractors Landscape
Associated with Boolean Gene Regulatory Networks | 15 pages, 8 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene regulatory network (GRN) modeling is a well-established theoretical
framework for the study of cell-fate specification during developmental
processes. Recently, dynamical models of GRNs have been taken as a basis for
formalizing the metaphorical model of Waddington's epigenetic landscape,
providing a natural extension for the general protocol of GRN modeling. In this
contribution we present in a coherent framework a novel implementation of two
previously proposed general frameworks for modeling the Epigenetic Attractors
Landscape associated with boolean GRNs: the inter-attractor and inter-state
transition approaches. We implement novel algorithms for estimating
inter-attractor transition probabilities without necessarily depending on
intensive single-event simulations. We analyze the performance and sensibility
to parameter choices of the algorithms for estimating inter-attractor
transition probabilities using three real GRN models. Additionally, we present
a side-by-side analysis of downstream analysis tools such as the attractors'
temporal and global ordering in the EAL. Overall, we show how the methods
complement each other using a real case study: a cellular-level GRN model for
epithelial carcinogenesis. We expect the toolkit and comparative analyses put
forward here to be a valuable additional re- source for the systems biology
community interested in modeling cellular differentiation and reprogramming
both in normal and pathological developmental processes.
| [
{
"created": "Wed, 14 Oct 2015 18:27:35 GMT",
"version": "v1"
}
] | 2015-10-15 | [
[
"Davila-Velderrain",
"Jose",
""
],
[
"Juarez-Ramiro",
"Luis",
""
],
[
"Martinez-Garcia",
"Juan C.",
""
],
[
"Alvarez-Buylla",
"Elena R.",
""
]
] | Gene regulatory network (GRN) modeling is a well-established theoretical framework for the study of cell-fate specification during developmental processes. Recently, dynamical models of GRNs have been taken as a basis for formalizing the metaphorical model of Waddington's epigenetic landscape, providing a natural extension for the general protocol of GRN modeling. In this contribution we present in a coherent framework a novel implementation of two previously proposed general frameworks for modeling the Epigenetic Attractors Landscape associated with boolean GRNs: the inter-attractor and inter-state transition approaches. We implement novel algorithms for estimating inter-attractor transition probabilities without necessarily depending on intensive single-event simulations. We analyze the performance and sensibility to parameter choices of the algorithms for estimating inter-attractor transition probabilities using three real GRN models. Additionally, we present a side-by-side analysis of downstream analysis tools such as the attractors' temporal and global ordering in the EAL. Overall, we show how the methods complement each other using a real case study: a cellular-level GRN model for epithelial carcinogenesis. We expect the toolkit and comparative analyses put forward here to be a valuable additional re- source for the systems biology community interested in modeling cellular differentiation and reprogramming both in normal and pathological developmental processes. |
1204.5941 | Andrei Zinovyev Dr. | Alexander N. Gorban, Annick Harel-Bellan, Nadya Morozova and Andrei
Zinovyev | Basic, simple and extendable kinetic model of protein synthesis | 22 pages, 9 figures | Mathematical Biosciences and Engineering 2019, Volume 16, Issue 6:
6602-6622 | 10.3934/mbe.2019329 | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Protein synthesis is one of the most fundamental biological processes, which
consumes a significant amount of cellular resources. Despite existence of
multiple mathematical models of translation, varying in the level of
mechanistical details, surprisingly, there is no basic and simple chemical
kinetic model of this process, derived directly from the detailed kinetic
model. One of the reasons for this is that the translation process is
characterized by indefinite number of states, thanks to existence of polysomes.
We bypass this difficulty by applying a trick consisting in lumping multiple
states of translated mRNA into few dynamical variables and by introducing a
variable describing the pool of translating ribosomes. The simplest model can
be solved analytically under some assumptions. The basic and simple model can
be extended, if necessary, to take into account various phenomena such as the
interaction between translating ribosomes, limited amount of ribosomal units or
regulation of translation by microRNA. The model can be used as a building
block (translation module) for more complex models of cellular processes. We
demonstrate the utility of the model in two examples. First, we determine the
critical parameters of the single protein synthesis for the case when the
ribosomal units are abundant. Second, we demonstrate intrinsic bi-stability in
the dynamics of the ribosomal protein turnover and predict that a minimal
number of ribosomes should pre-exists in a living cell to sustain its protein
synthesis machinery, even in the absence of proliferation.
| [
{
"created": "Thu, 26 Apr 2012 14:31:41 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Jan 2013 23:20:27 GMT",
"version": "v2"
},
{
"created": "Mon, 29 Apr 2019 15:49:21 GMT",
"version": "v3"
}
] | 2021-01-06 | [
[
"Gorban",
"Alexander N.",
""
],
[
"Harel-Bellan",
"Annick",
""
],
[
"Morozova",
"Nadya",
""
],
[
"Zinovyev",
"Andrei",
""
]
] | Protein synthesis is one of the most fundamental biological processes, which consumes a significant amount of cellular resources. Despite existence of multiple mathematical models of translation, varying in the level of mechanistical details, surprisingly, there is no basic and simple chemical kinetic model of this process, derived directly from the detailed kinetic model. One of the reasons for this is that the translation process is characterized by indefinite number of states, thanks to existence of polysomes. We bypass this difficulty by applying a trick consisting in lumping multiple states of translated mRNA into few dynamical variables and by introducing a variable describing the pool of translating ribosomes. The simplest model can be solved analytically under some assumptions. The basic and simple model can be extended, if necessary, to take into account various phenomena such as the interaction between translating ribosomes, limited amount of ribosomal units or regulation of translation by microRNA. The model can be used as a building block (translation module) for more complex models of cellular processes. We demonstrate the utility of the model in two examples. First, we determine the critical parameters of the single protein synthesis for the case when the ribosomal units are abundant. Second, we demonstrate intrinsic bi-stability in the dynamics of the ribosomal protein turnover and predict that a minimal number of ribosomes should pre-exists in a living cell to sustain its protein synthesis machinery, even in the absence of proliferation. |
2406.15142 | Tiziana Cattai | Tiziana Cattai, Gaetano Scarano, Marie-Constance Corsi, Fabrizio De
Vico Fallani, Stefania Colonnese | Community Detection from Multiple Observations: from Product Graph Model
to Brain Applications | This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a multilayer graph model for the community detection from
multiple observations. This is a very frequent situation, when different
estimators are applied to infer graph edges from signals at its nodes, or when
different signal measurements are carried out. The multilayer network stacks
the graph observations at the different layers, and it links replica nodes at
adjacent layers. This configuration matches the Cartesian product between the
ground truth graph and a path graph, where the number of nodes corresponds to
the number of the observations. Stemming on the algebraic structure of the
Laplacian of the Cartesian multilayer network, we infer a subset of the
eigenvectors of the true graph and perform community detection. Experimental
results on synthetic graphs prove the accuracy of the method, which outperforms
state-of-the-art approaches in terms of ability of correctly detecting graph
communities. Finally, we show the application of our method to discriminate
different brain networks derived from real EEG data collected during motor
imagery experiments. We conclude that our approach appears promising in
identifying graph communities when multiple observations of the graph are
available and it results promising for EEG-based motor imagery applications.
| [
{
"created": "Fri, 21 Jun 2024 13:36:32 GMT",
"version": "v1"
}
] | 2024-06-24 | [
[
"Cattai",
"Tiziana",
""
],
[
"Scarano",
"Gaetano",
""
],
[
"Corsi",
"Marie-Constance",
""
],
[
"Fallani",
"Fabrizio De Vico",
""
],
[
"Colonnese",
"Stefania",
""
]
] | This paper proposes a multilayer graph model for the community detection from multiple observations. This is a very frequent situation, when different estimators are applied to infer graph edges from signals at its nodes, or when different signal measurements are carried out. The multilayer network stacks the graph observations at the different layers, and it links replica nodes at adjacent layers. This configuration matches the Cartesian product between the ground truth graph and a path graph, where the number of nodes corresponds to the number of the observations. Stemming on the algebraic structure of the Laplacian of the Cartesian multilayer network, we infer a subset of the eigenvectors of the true graph and perform community detection. Experimental results on synthetic graphs prove the accuracy of the method, which outperforms state-of-the-art approaches in terms of ability of correctly detecting graph communities. Finally, we show the application of our method to discriminate different brain networks derived from real EEG data collected during motor imagery experiments. We conclude that our approach appears promising in identifying graph communities when multiple observations of the graph are available and it results promising for EEG-based motor imagery applications. |
2109.05688 | Rahimah Zakaria | Nazlahshaniza Shafina, Che Aishah Nazariah Ismaila, Mohd Zulkifli
Mustafa, Nurhafizah Ghani, Asma Hayati Ahmad, Zahiruddin Othman, Adi Wijaya,
Rahimah Zakaria | Thematic analysis of multiple sclerosis research by enhanced strategic
diagram | 20 pages,6 figures | null | null | null | q-bio.NC cs.DL | http://creativecommons.org/licenses/by-sa/4.0/ | This bibliometric review summarised the research trends and analysed research
areas in multiple sclerosis (MS) over the last decade. The documents containing
the term "multiple sclerosis" in the article title were retrieved from the
Scopus database. We found a total of 18003 articles published in journals in
the English language between 2012 and 2021. The emerging keywords identified
utilising the enhanced strategic diagram were "covid-19", "teriflunomide",
"clinical trial", "microglia", "b cells", "myelin", "brain", "white matter",
"functional connectivity", "pain", "employment", "health-related quality of
life", "meta-analysis" and "comorbidity". In conclusion, this study
demonstrates the tremendous growth of MS literature worldwide, which is
expected to grow more than double during the next decade especially in the
identified emerging topics.
| [
{
"created": "Mon, 13 Sep 2021 03:40:12 GMT",
"version": "v1"
}
] | 2021-09-14 | [
[
"Shafina",
"Nazlahshaniza",
""
],
[
"Ismaila",
"Che Aishah Nazariah",
""
],
[
"Mustafa",
"Mohd Zulkifli",
""
],
[
"Ghani",
"Nurhafizah",
""
],
[
"Ahmad",
"Asma Hayati",
""
],
[
"Othman",
"Zahiruddin",
""
],
[
"Wijaya",
"Adi",
""
],
[
"Zakaria",
"Rahimah",
""
]
] | This bibliometric review summarised the research trends and analysed research areas in multiple sclerosis (MS) over the last decade. The documents containing the term "multiple sclerosis" in the article title were retrieved from the Scopus database. We found a total of 18003 articles published in journals in the English language between 2012 and 2021. The emerging keywords identified utilising the enhanced strategic diagram were "covid-19", "teriflunomide", "clinical trial", "microglia", "b cells", "myelin", "brain", "white matter", "functional connectivity", "pain", "employment", "health-related quality of life", "meta-analysis" and "comorbidity". In conclusion, this study demonstrates the tremendous growth of MS literature worldwide, which is expected to grow more than double during the next decade especially in the identified emerging topics. |
0904.4499 | Kaushik Majumdar | Kaushik Majumdar | Fourier Uniformity: An Useful Tool for Analyzing EEG Signals with An
Application to Source Localization | Accepted for oral presententation in the International Joint
Conference of Neural Networks 2009, Atlanta, USA. It will not be included in
the proceedings for the author's inability to attend the conference | null | null | IJCNN09 submission # 17 | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | If two signals are phase synchronous then the respective Fourier component at
each spectral band should exhibit certain properties. In a pair of artificially
generated phase synchronous signals the phase difference at each frequency band
changes very slowly over the subsequent frequency bands. This has been called
Fourier uniformity in this paper and a measure of it has been proposed. An
usefulness of this measure has been outlined in the case of cortical source
localization of scalp EEG.
| [
{
"created": "Tue, 28 Apr 2009 22:13:12 GMT",
"version": "v1"
}
] | 2009-04-30 | [
[
"Majumdar",
"Kaushik",
""
]
] | If two signals are phase synchronous then the respective Fourier component at each spectral band should exhibit certain properties. In a pair of artificially generated phase synchronous signals the phase difference at each frequency band changes very slowly over the subsequent frequency bands. This has been called Fourier uniformity in this paper and a measure of it has been proposed. An usefulness of this measure has been outlined in the case of cortical source localization of scalp EEG. |
2211.07374 | Fateme Ghayem Dr. | Fateme Ghayem, Hanlu Yang, Furkan Kantar, Seung-Jun Kim, Vince D.
Calhoun, Tulay Adali | New Interpretable Patterns and Discriminative Features from Brain
Functional Network Connectivity Using Dictionary Learning | null | null | null | null | q-bio.NC cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Independent component analysis (ICA) of multi-subject functional magnetic
resonance imaging (fMRI) data has proven useful in providing a fully
multivariate summary that can be used for multiple purposes. ICA can identify
patterns that can discriminate between healthy controls (HC) and patients with
various mental disorders such as schizophrenia (Sz). Temporal functional
network connectivity (tFNC) obtained from ICA can effectively explain the
interactions between brain networks. On the other hand, dictionary learning
(DL) enables the discovery of hidden information in data using learnable basis
signals through the use of sparsity. In this paper, we present a new method
that leverages ICA and DL for the identification of directly interpretable
patterns to discriminate between the HC and Sz groups. We use multi-subject
resting-state fMRI data from $358$ subjects and form subject-specific tFNC
feature vectors from ICA results. Then, we learn sparse representations of the
tFNCs and introduce a new set of sparse features as well as new interpretable
patterns from the learned atoms. Our experimental results show that the new
representation not only leads to effective classification between HC and Sz
groups using sparse features, but can also identify new interpretable patterns
from the learned atoms that can help understand the complexities of mental
diseases such as schizophrenia.
| [
{
"created": "Thu, 10 Nov 2022 19:49:16 GMT",
"version": "v1"
}
] | 2022-11-15 | [
[
"Ghayem",
"Fateme",
""
],
[
"Yang",
"Hanlu",
""
],
[
"Kantar",
"Furkan",
""
],
[
"Kim",
"Seung-Jun",
""
],
[
"Calhoun",
"Vince D.",
""
],
[
"Adali",
"Tulay",
""
]
] | Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminate between healthy controls (HC) and patients with various mental disorders such as schizophrenia (Sz). Temporal functional network connectivity (tFNC) obtained from ICA can effectively explain the interactions between brain networks. On the other hand, dictionary learning (DL) enables the discovery of hidden information in data using learnable basis signals through the use of sparsity. In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups. We use multi-subject resting-state fMRI data from $358$ subjects and form subject-specific tFNC feature vectors from ICA results. Then, we learn sparse representations of the tFNCs and introduce a new set of sparse features as well as new interpretable patterns from the learned atoms. Our experimental results show that the new representation not only leads to effective classification between HC and Sz groups using sparse features, but can also identify new interpretable patterns from the learned atoms that can help understand the complexities of mental diseases such as schizophrenia. |
2107.08926 | Breno Ferraz de Oliveira | D. Bazeia, M. Bongestab, B.F. de Oliveira and A. Szolnoki | Effects of a pestilent species on the stability of cyclically dominant
species | 10 two-column pages, 7 figures, accepted for publication in Chaos,
Solitons, and Fractals | Chaos, Solitons and Fractals 151 (2021) 111255 | 10.1016/j.chaos.2021.111255 | null | q-bio.PE cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | Cyclic dominance is frequently believed to be a mechanism that maintains
diversity of competing species. But this delicate balance could also be fragile
if some of the members is weakened because an extinction of a species will
involve the annihilation of its predator hence leaving only a single species
alive. To check this expectation we here introduce a fourth species which
chases exclusively a single member of the basic model composed by three
cyclically dominant species. Interestingly, the coexistence is not necessarily
broken and we have detected three consecutive phase transitions as we vary only
the invasion strength of the fourth pestilent species. The resulting phases are
analyzed by different techniques including the study of the Hamming distance
density profiles. Some of our observations strengthen previous findings about
cyclically dominant system, but they also offer new revelations and
counter-intuitive phenomenon, like supporting pestilent species may result in
its extinction, hence enriching our understanding about these very simple but
still surprisingly complex systems.
| [
{
"created": "Mon, 19 Jul 2021 14:39:54 GMT",
"version": "v1"
}
] | 2021-08-31 | [
[
"Bazeia",
"D.",
""
],
[
"Bongestab",
"M.",
""
],
[
"de Oliveira",
"B. F.",
""
],
[
"Szolnoki",
"A.",
""
]
] | Cyclic dominance is frequently believed to be a mechanism that maintains diversity of competing species. But this delicate balance could also be fragile if some of the members is weakened because an extinction of a species will involve the annihilation of its predator hence leaving only a single species alive. To check this expectation we here introduce a fourth species which chases exclusively a single member of the basic model composed by three cyclically dominant species. Interestingly, the coexistence is not necessarily broken and we have detected three consecutive phase transitions as we vary only the invasion strength of the fourth pestilent species. The resulting phases are analyzed by different techniques including the study of the Hamming distance density profiles. Some of our observations strengthen previous findings about cyclically dominant system, but they also offer new revelations and counter-intuitive phenomenon, like supporting pestilent species may result in its extinction, hence enriching our understanding about these very simple but still surprisingly complex systems. |
1803.05025 | Mara Scussolini | Mara Scussolini, Vanessa Cossu, Cecilia Marini, Gianmario Sambuceti,
Giacomo Caviglia | FDG kinetics in cells and tissues: a biochemically-driven compartmental
approach | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The radioactive glucose analogue 2-deoxy-2-[18F]fluoro-D-glucose (FDG) is
widely used to reconstruct glucose metabolism and other biological functions in
cells and tissues. The analysis of data on the time course of FDG tracer
distribution is performed by the use of appropriate compartmental models.
Motivated by recent results in cell biochemistry, we describe a new
compartmental model aiming at the reconstruction of tracer kinetics in cells
and tissues, which emphasizes the different roles of the cytosol and of the
endoplasmic reticulum. Two applications of the new model are examined, that are
concerned with real data from cancer cell cultures in vitro, and cancer tissues
in vivo. The results are compared with those obtained through application of
more standard compartmental models against the same datasets and appear to be
in a better agreement with respect to recent biochemical experimental evidence.
In particular, it is shown that tracer tends to accumulate in the endoplasmic
reticulum, rather than cytosol, and that the rate of phosphorylation is higher
than predicted by current models.
| [
{
"created": "Tue, 13 Mar 2018 19:57:28 GMT",
"version": "v1"
}
] | 2018-03-15 | [
[
"Scussolini",
"Mara",
""
],
[
"Cossu",
"Vanessa",
""
],
[
"Marini",
"Cecilia",
""
],
[
"Sambuceti",
"Gianmario",
""
],
[
"Caviglia",
"Giacomo",
""
]
] | The radioactive glucose analogue 2-deoxy-2-[18F]fluoro-D-glucose (FDG) is widely used to reconstruct glucose metabolism and other biological functions in cells and tissues. The analysis of data on the time course of FDG tracer distribution is performed by the use of appropriate compartmental models. Motivated by recent results in cell biochemistry, we describe a new compartmental model aiming at the reconstruction of tracer kinetics in cells and tissues, which emphasizes the different roles of the cytosol and of the endoplasmic reticulum. Two applications of the new model are examined, that are concerned with real data from cancer cell cultures in vitro, and cancer tissues in vivo. The results are compared with those obtained through application of more standard compartmental models against the same datasets and appear to be in a better agreement with respect to recent biochemical experimental evidence. In particular, it is shown that tracer tends to accumulate in the endoplasmic reticulum, rather than cytosol, and that the rate of phosphorylation is higher than predicted by current models. |
1601.00909 | Mihai Alexandru Petrovici | Mihai A. Petrovici, Ilja Bytschok, Johannes Bill, Johannes Schemmel
and Karlheinz Meier | The high-conductance state enables neural sampling in networks of LIF
neurons | 3 pages, 1 figure | null | 10.1186/1471-2202-16-S1-O2 | null | q-bio.NC cond-mat.dis-nn cs.NE physics.bio-ph stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The apparent stochasticity of in-vivo neural circuits has long been
hypothesized to represent a signature of ongoing stochastic inference in the
brain. More recently, a theoretical framework for neural sampling has been
proposed, which explains how sample-based inference can be performed by
networks of spiking neurons. One particular requirement of this approach is
that the neural response function closely follows a logistic curve.
Analytical approaches to calculating neural response functions have been the
subject of many theoretical studies. In order to make the problem tractable,
particular assumptions regarding the neural or synaptic parameters are usually
made. However, biologically significant activity regimes exist which are not
covered by these approaches: Under strong synaptic bombardment, as is often the
case in cortex, the neuron is shifted into a high-conductance state (HCS)
characterized by a small membrane time constant. In this regime, synaptic time
constants and refractory periods dominate membrane dynamics.
The core idea of our approach is to separately consider two different "modes"
of spiking dynamics: burst spiking and transient quiescence, in which the
neuron does not spike for longer periods. We treat the former by propagating
the PDF of the effective membrane potential from spike to spike within a burst,
while using a diffusion approximation for the latter. We find that our
prediction of the neural response function closely matches simulation data.
Moreover, in the HCS scenario, we show that the neural response function
becomes symmetric and can be well approximated by a logistic function, thereby
providing the correct dynamics in order to perform neural sampling. We hereby
provide not only a normative framework for Bayesian inference in cortex, but
also powerful applications of low-power, accelerated neuromorphic systems to
relevant machine learning tasks.
| [
{
"created": "Tue, 5 Jan 2016 17:15:37 GMT",
"version": "v1"
}
] | 2017-03-14 | [
[
"Petrovici",
"Mihai A.",
""
],
[
"Bytschok",
"Ilja",
""
],
[
"Bill",
"Johannes",
""
],
[
"Schemmel",
"Johannes",
""
],
[
"Meier",
"Karlheinz",
""
]
] | The apparent stochasticity of in-vivo neural circuits has long been hypothesized to represent a signature of ongoing stochastic inference in the brain. More recently, a theoretical framework for neural sampling has been proposed, which explains how sample-based inference can be performed by networks of spiking neurons. One particular requirement of this approach is that the neural response function closely follows a logistic curve. Analytical approaches to calculating neural response functions have been the subject of many theoretical studies. In order to make the problem tractable, particular assumptions regarding the neural or synaptic parameters are usually made. However, biologically significant activity regimes exist which are not covered by these approaches: Under strong synaptic bombardment, as is often the case in cortex, the neuron is shifted into a high-conductance state (HCS) characterized by a small membrane time constant. In this regime, synaptic time constants and refractory periods dominate membrane dynamics. The core idea of our approach is to separately consider two different "modes" of spiking dynamics: burst spiking and transient quiescence, in which the neuron does not spike for longer periods. We treat the former by propagating the PDF of the effective membrane potential from spike to spike within a burst, while using a diffusion approximation for the latter. We find that our prediction of the neural response function closely matches simulation data. Moreover, in the HCS scenario, we show that the neural response function becomes symmetric and can be well approximated by a logistic function, thereby providing the correct dynamics in order to perform neural sampling. We hereby provide not only a normative framework for Bayesian inference in cortex, but also powerful applications of low-power, accelerated neuromorphic systems to relevant machine learning tasks. |
q-bio/0503008 | Michael Stumpf | Ino Agrafioti, Jonathan Swire, James Abbott, Derek Huntley, Sarah
Butcher and Michael P.H. Stumpf | Comparative Analysis of the Saccharomyces cerevisiae and Caenorhabditis
elegans Protein Interaction Network | Accepted for publication in BMC Evolutionary Biology | BMC Evolutionary Biology 2005, 5:23 | null | null | q-bio.MN cond-mat.dis-nn q-bio.PE | null | Protein interaction networks aim to summarize the complex interplay of
proteins in an organism. Early studies suggested that the position of a protein
in the network determines its evolutionary rate but there has been considerable
disagreement as to what extent other factors, such as protein abundance, modify
this reported dependence.
We compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans
with those of closely related species to elucidate the recent evolutionary
history of their respective protein interaction networks. Interaction and
expression data are studied in the light of a detailed phylogenetic analysis.
The underlying network structure is incorporated explicitly into the
statistical analysis.
The increased phylogenetic resolution, paired with high-quality interaction
data, allows us to resolve the way in which protein interaction network
structure and abundance of proteins affect the evolutionary rate. We find that
expression levels are better predictors of the evolutionary rate than a
protein's connectivity. Detailed analysis of the two organisms also shows that
the evolutionary rates of interacting proteins are not sufficiently similar to
be mutually predictive.
It appears that meaningful inferences about the evolution of protein
interaction networks require comparative analysis of reasonably closely related
species. The signature of protein evolution is shaped by a protein's abundance
in the organism and its function and the biological process it is involved in.
Its position in the interaction networks and its connectivity may modulate this
but they appear to have only minor influence on a protein's evolutionary rate.
| [
{
"created": "Thu, 3 Mar 2005 09:50:24 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Agrafioti",
"Ino",
""
],
[
"Swire",
"Jonathan",
""
],
[
"Abbott",
"James",
""
],
[
"Huntley",
"Derek",
""
],
[
"Butcher",
"Sarah",
""
],
[
"Stumpf",
"Michael P. H.",
""
]
] | Protein interaction networks aim to summarize the complex interplay of proteins in an organism. Early studies suggested that the position of a protein in the network determines its evolutionary rate but there has been considerable disagreement as to what extent other factors, such as protein abundance, modify this reported dependence. We compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans with those of closely related species to elucidate the recent evolutionary history of their respective protein interaction networks. Interaction and expression data are studied in the light of a detailed phylogenetic analysis. The underlying network structure is incorporated explicitly into the statistical analysis. The increased phylogenetic resolution, paired with high-quality interaction data, allows us to resolve the way in which protein interaction network structure and abundance of proteins affect the evolutionary rate. We find that expression levels are better predictors of the evolutionary rate than a protein's connectivity. Detailed analysis of the two organisms also shows that the evolutionary rates of interacting proteins are not sufficiently similar to be mutually predictive. It appears that meaningful inferences about the evolution of protein interaction networks require comparative analysis of reasonably closely related species. The signature of protein evolution is shaped by a protein's abundance in the organism and its function and the biological process it is involved in. Its position in the interaction networks and its connectivity may modulate this but they appear to have only minor influence on a protein's evolutionary rate. |
1501.04383 | Liane Gabora | Liane Gabora | The 'Power of Then': The Uniquely Human Capacity to Imagine Beyond the
Present | 6 pages, Psychology Today (online).
http://www.psychologytoday.com/blog/mindbloggling (2010) | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans appear to be uniquely in their ability to transcend the present and
reflect on current ideas in terms of what was experienced in the past, or
fantasize about the future. This paper presents an account, in layperson terms,
of how this ability came about, its importance in modern life, and why it
defines our human-ness.
| [
{
"created": "Mon, 19 Jan 2015 04:25:00 GMT",
"version": "v1"
}
] | 2015-01-20 | [
[
"Gabora",
"Liane",
""
]
] | Humans appear to be uniquely in their ability to transcend the present and reflect on current ideas in terms of what was experienced in the past, or fantasize about the future. This paper presents an account, in layperson terms, of how this ability came about, its importance in modern life, and why it defines our human-ness. |
q-bio/0502002 | Chaitanya Athale | Chaitanya A. Athale and Thomas S. Deisboeck | The Effects of EGF-Receptor Density on Multiscale Tumor Growth Patterns | null | null | null | null | q-bio.CB q-bio.MN q-bio.OT | null | We studied the effects of epidermal growth factor receptor (EGFR) density on
tumor growth dynamics, both on the sub- and the multi-cellular level using our
previously developed model. This algorithm simulates the growth of a brain
tumor using a multi-scale two-dimensional agent-based approach with an
integrated transforming growth factor alpha (TGFalpha) induced
EGFR-gene-protein interaction network. The results confirm that increasing cell
receptor density correlates with an acceleration of the tumor system's
spatio-temporal expansion dynamics. This multicellular behavior cannot be
explained solely on the basis of spatial sub-cellular dynamics, which remain
qualitatively similar amongst the three glioma cell lines investigated here in
silico. Rather, we find that cells with higher EGFR density show an early
increase in the phenotypic switching activity between proliferative and
migratory traits, linked to a higher level of initial auto-stimulation by the
PLCgamma-mediated TGFalpha-EGFR autocrine network. This indicates a more active
protein level interaction in these chemotactically acting tumor systems and
supports the role of post-translational regulation for the implemented EGFR
pathway. Implications of these results for experimental cancer research are
discussed.
| [
{
"created": "Tue, 1 Feb 2005 22:48:58 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Athale",
"Chaitanya A.",
""
],
[
"Deisboeck",
"Thomas S.",
""
]
] | We studied the effects of epidermal growth factor receptor (EGFR) density on tumor growth dynamics, both on the sub- and the multi-cellular level using our previously developed model. This algorithm simulates the growth of a brain tumor using a multi-scale two-dimensional agent-based approach with an integrated transforming growth factor alpha (TGFalpha) induced EGFR-gene-protein interaction network. The results confirm that increasing cell receptor density correlates with an acceleration of the tumor system's spatio-temporal expansion dynamics. This multicellular behavior cannot be explained solely on the basis of spatial sub-cellular dynamics, which remain qualitatively similar amongst the three glioma cell lines investigated here in silico. Rather, we find that cells with higher EGFR density show an early increase in the phenotypic switching activity between proliferative and migratory traits, linked to a higher level of initial auto-stimulation by the PLCgamma-mediated TGFalpha-EGFR autocrine network. This indicates a more active protein level interaction in these chemotactically acting tumor systems and supports the role of post-translational regulation for the implemented EGFR pathway. Implications of these results for experimental cancer research are discussed. |
1503.07248 | Leandro Silva | L. A. da Silva, R. D. Vilela | Colored noise and memory effects on formal spiking neuron models | 16 pages, 10 figures | Phys. Rev. E 91, 062702 (2015) | 10.1103/PhysRevE.91.062702 | null | q-bio.NC cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simplified neuronal models capture the essence of the electrical activity of
a generic neuron, besides being more interesting from the computational point
of view when compared to higher dimensional models such as the Hodgkin-Huxley
one. In this work, we propose a generalized resonate-and-fire model described
by a generalized Langevin equation that takes into account memory effects and
colored noise. We perform a comprehensive numerical analysis to study the
dynamics and the point process statistics of the proposed model, highlighting
interesting new features like: i) non-monotonic behavior (emergence of peak
structures, enhanced by the choice of colored noise characteristic time-scale)
of the coefficient of variation (CV) as a function of memory characteristic
time-scale, ii) colored noise-induced shift in the CV, and iii) emergence and
suppression of multimodality in the interspike interval (ISI) distribution due
to memory-induced subthreshold oscillations. Moreover, in the noise-induced
spike regime, we study how memory and colored noise affects the coherence
resonance (CR) phenomenon. We found that for sufficiently long memory, CR is
not only suppressed, but also the minimum of the CV $\times$ noise intensity
curve that characterizes the presence of CR may be replaced by a maximum. The
aforementioned features allow to interpret the interplay between memory and
colored noise as an effective control mechanism to neuronal variability. Since
both variability and non-trivial temporal patterns in the ISI distribution are
ubiquitous in biological cells, we hope the present model can be useful in
modeling real aspects of neurons.
| [
{
"created": "Wed, 25 Mar 2015 00:50:23 GMT",
"version": "v1"
},
{
"created": "Tue, 12 May 2015 23:09:42 GMT",
"version": "v2"
}
] | 2015-06-17 | [
[
"da Silva",
"L. A.",
""
],
[
"Vilela",
"R. D.",
""
]
] | Simplified neuronal models capture the essence of the electrical activity of a generic neuron, besides being more interesting from the computational point of view when compared to higher dimensional models such as the Hodgkin-Huxley one. In this work, we propose a generalized resonate-and-fire model described by a generalized Langevin equation that takes into account memory effects and colored noise. We perform a comprehensive numerical analysis to study the dynamics and the point process statistics of the proposed model, highlighting interesting new features like: i) non-monotonic behavior (emergence of peak structures, enhanced by the choice of colored noise characteristic time-scale) of the coefficient of variation (CV) as a function of memory characteristic time-scale, ii) colored noise-induced shift in the CV, and iii) emergence and suppression of multimodality in the interspike interval (ISI) distribution due to memory-induced subthreshold oscillations. Moreover, in the noise-induced spike regime, we study how memory and colored noise affects the coherence resonance (CR) phenomenon. We found that for sufficiently long memory, CR is not only suppressed, but also the minimum of the CV $\times$ noise intensity curve that characterizes the presence of CR may be replaced by a maximum. The aforementioned features allow to interpret the interplay between memory and colored noise as an effective control mechanism to neuronal variability. Since both variability and non-trivial temporal patterns in the ISI distribution are ubiquitous in biological cells, we hope the present model can be useful in modeling real aspects of neurons. |
1707.05722 | Sebastian Roch | Sebastien Roch and Kun-Chieh Wang | Circular Networks from Distorted Metrics | Submitted | null | null | null | q-bio.PE cs.DM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Trees have long been used as a graphical representation of species
relationships. However complex evolutionary events, such as genetic
reassortments or hybrid speciations which occur commonly in viruses, bacteria
and plants, do not fit into this elementary framework. Alternatively, various
network representations have been developed. Circular networks are a natural
generalization of leaf-labeled trees interpreted as split systems, that is,
collections of bipartitions over leaf labels corresponding to current species.
Although such networks do not explicitly model specific evolutionary events of
interest, their straightforward visualization and fast reconstruction have made
them a popular exploratory tool to detect network-like evolution in genetic
datasets.
Standard reconstruction methods for circular networks, such as Neighbor-Net,
rely on an associated metric on the species set. Such a metric is first
estimated from DNA sequences, which leads to a key difficulty: distantly
related sequences produce statistically unreliable estimates. This is
problematic for Neighbor-Net as it is based on the popular tree reconstruction
method Neighbor-Joining, whose sensitivity to distance estimation errors is
well established theoretically. In the tree case, more robust reconstruction
methods have been developed using the notion of a distorted metric, which
captures the dependence of the error in the distance through a radius of
accuracy. Here we design the first circular network reconstruction method based
on distorted metrics. Our method is computationally efficient. Moreover, the
analysis of its radius of accuracy highlights the important role played by the
maximum incompatibility, a measure of the extent to which the network differs
from a tree.
| [
{
"created": "Tue, 18 Jul 2017 16:04:19 GMT",
"version": "v1"
}
] | 2017-07-24 | [
[
"Roch",
"Sebastien",
""
],
[
"Wang",
"Kun-Chieh",
""
]
] | Trees have long been used as a graphical representation of species relationships. However complex evolutionary events, such as genetic reassortments or hybrid speciations which occur commonly in viruses, bacteria and plants, do not fit into this elementary framework. Alternatively, various network representations have been developed. Circular networks are a natural generalization of leaf-labeled trees interpreted as split systems, that is, collections of bipartitions over leaf labels corresponding to current species. Although such networks do not explicitly model specific evolutionary events of interest, their straightforward visualization and fast reconstruction have made them a popular exploratory tool to detect network-like evolution in genetic datasets. Standard reconstruction methods for circular networks, such as Neighbor-Net, rely on an associated metric on the species set. Such a metric is first estimated from DNA sequences, which leads to a key difficulty: distantly related sequences produce statistically unreliable estimates. This is problematic for Neighbor-Net as it is based on the popular tree reconstruction method Neighbor-Joining, whose sensitivity to distance estimation errors is well established theoretically. In the tree case, more robust reconstruction methods have been developed using the notion of a distorted metric, which captures the dependence of the error in the distance through a radius of accuracy. Here we design the first circular network reconstruction method based on distorted metrics. Our method is computationally efficient. Moreover, the analysis of its radius of accuracy highlights the important role played by the maximum incompatibility, a measure of the extent to which the network differs from a tree. |
2205.12269 | Ariel Nikas | Ariel Nikas, Hasan Ahmed, and Veronika I. Zarnitsyna | Estimating Waning of Vaccine Effectiveness: a Simulation Study | 25 pages, 6 figures, Submitted to Clinical Infectious Disease | null | null | null | q-bio.QM q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Developing accurate and reliable methods to estimate vaccine protection is a
key goal in immunology and public health. While several statistical methods
have been proposed, their potential inaccuracy in capturing fast intra-seasonal
waning of vaccine-induced protection needs to be rigorously investigated. To
compare statistical methods for vaccine effectiveness (VE) estimation, we
generated simulated data using a multiscale agent-based model of an epidemic
with an acute viral infection and differing extents of VE waning. We extended
the previously proposed framework for VE measures based on the observational
data richness to assess changes of vaccine-induced protection with time. While
VE measures based on hard-to-collect information (e.g. exact timing of
exposures) were accurate, usually VE studies rely on time-to-infection data and
the Cox proportional hazard model. We found that its extension utilizing scaled
Schoenfeld residuals, previously proposed for capturing VE waning, was
unreliable in capturing both the degree of waning and its functional form and
identified the mathematical factors contributing to this unreliability. We
showed that partitioning time and including a time-vaccine interaction term in
the Cox model significantly improved estimation of VE waning, even in the case
of dramatic, rapid waning. We also proposed how to optimize the partitioning
scheme. Using simulated data, we compared different measures of VE for
capturing the intra-seasonal waning of vaccine-induced protection. We propose
an extension of the Cox model based on including a time-vaccine interaction
term with further optimization of partitioning time. These findings may guide
future analysis of VE waning in observational data.
| [
{
"created": "Tue, 24 May 2022 13:59:28 GMT",
"version": "v1"
}
] | 2022-05-26 | [
[
"Nikas",
"Ariel",
""
],
[
"Ahmed",
"Hasan",
""
],
[
"Zarnitsyna",
"Veronika I.",
""
]
] | Developing accurate and reliable methods to estimate vaccine protection is a key goal in immunology and public health. While several statistical methods have been proposed, their potential inaccuracy in capturing fast intra-seasonal waning of vaccine-induced protection needs to be rigorously investigated. To compare statistical methods for vaccine effectiveness (VE) estimation, we generated simulated data using a multiscale agent-based model of an epidemic with an acute viral infection and differing extents of VE waning. We extended the previously proposed framework for VE measures based on the observational data richness to assess changes of vaccine-induced protection with time. While VE measures based on hard-to-collect information (e.g. exact timing of exposures) were accurate, usually VE studies rely on time-to-infection data and the Cox proportional hazard model. We found that its extension utilizing scaled Schoenfeld residuals, previously proposed for capturing VE waning, was unreliable in capturing both the degree of waning and its functional form and identified the mathematical factors contributing to this unreliability. We showed that partitioning time and including a time-vaccine interaction term in the Cox model significantly improved estimation of VE waning, even in the case of dramatic, rapid waning. We also proposed how to optimize the partitioning scheme. Using simulated data, we compared different measures of VE for capturing the intra-seasonal waning of vaccine-induced protection. We propose an extension of the Cox model based on including a time-vaccine interaction term with further optimization of partitioning time. These findings may guide future analysis of VE waning in observational data. |
1511.01060 | Sergio Gabriel Quesada Acuna | Alejandro Sol\'orzano, Luis D. G\'omez, Juli\'an Monge-N\'ajera and
Brian I. Crother | Redescription and validation of Bothriechis supraciliaris (Serpentes:
Viperidae) | 9 pages, 11 figures | Rev. Biol. Trop., 46(2): 453-462, 1998 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The populations of pitvipers from south western Costa Rica, have
traditionally been identified as Bothriechis schlegelii (Berthold). However, in
1954 E. H. Taylor described one specimen from the area as a new subspecies, B.
schlegelii supraciliaris. Werman returned supraciliaris to synonymy with
schlegelii four decades later. However, morphometry and color pattern in a SW
Costa Rica population (25 specimens) differ from those of specimens (N=57) from
other parts of Costa Rica and from descriptions of South American specimens.
Here the epithet Bothriechis schlegelii supraciliaris Taylor 1954. is
reestablished as a valid taxon and elevated to specific rank as B.
supraciliaris stat.nov. It is closely related to B. schlegelii from which it
differs by its color patterns based on a uniform ground color with polymorphic
dorsal designs, and its lower counts of ventral and caudal scales.
| [
{
"created": "Tue, 3 Nov 2015 20:06:48 GMT",
"version": "v1"
}
] | 2015-11-04 | [
[
"Solórzano",
"Alejandro",
""
],
[
"Gómez",
"Luis D.",
""
],
[
"Monge-Nájera",
"Julián",
""
],
[
"Crother",
"Brian I.",
""
]
] | The populations of pitvipers from south western Costa Rica, have traditionally been identified as Bothriechis schlegelii (Berthold). However, in 1954 E. H. Taylor described one specimen from the area as a new subspecies, B. schlegelii supraciliaris. Werman returned supraciliaris to synonymy with schlegelii four decades later. However, morphometry and color pattern in a SW Costa Rica population (25 specimens) differ from those of specimens (N=57) from other parts of Costa Rica and from descriptions of South American specimens. Here the epithet Bothriechis schlegelii supraciliaris Taylor 1954. is reestablished as a valid taxon and elevated to specific rank as B. supraciliaris stat.nov. It is closely related to B. schlegelii from which it differs by its color patterns based on a uniform ground color with polymorphic dorsal designs, and its lower counts of ventral and caudal scales. |
1902.04704 | Tal Golan | Nikolaus Kriegeskorte and Tal Golan | Neural network models and deep learning - a primer for biologists | 14 pages, 4 figures; added references, minor corrections | Current Biology 29(7) (2019) R231-R236 | 10.1016/j.cub.2019.02.034 | null | q-bio.NC cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Originally inspired by neurobiology, deep neural network models have become a
powerful tool of machine learning and artificial intelligence, where they are
used to approximate functions and dynamics by learning from examples. Here we
give a brief introduction to neural network models and deep learning for
biologists. We introduce feedforward and recurrent networks and explain the
expressive power of this modeling framework and the backpropagation algorithm
for setting the parameters. Finally, we consider how deep neural networks might
help us understand the brain's computations.
| [
{
"created": "Wed, 13 Feb 2019 02:09:26 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Mar 2019 00:19:24 GMT",
"version": "v2"
}
] | 2019-04-12 | [
[
"Kriegeskorte",
"Nikolaus",
""
],
[
"Golan",
"Tal",
""
]
] | Originally inspired by neurobiology, deep neural network models have become a powerful tool of machine learning and artificial intelligence, where they are used to approximate functions and dynamics by learning from examples. Here we give a brief introduction to neural network models and deep learning for biologists. We introduce feedforward and recurrent networks and explain the expressive power of this modeling framework and the backpropagation algorithm for setting the parameters. Finally, we consider how deep neural networks might help us understand the brain's computations. |
2105.03617 | Masahito Ohue | Masahito Ohue, Yutaka Akiyama | MEGADOCK-GUI: a GUI-based complete cross-docking tool for exploring
protein-protein interactions | 9 pages, 6 figures | null | null | null | q-bio.BM cs.DC q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information on protein-protein interactions (PPIs) not only advances our
understanding of molecular biology but also provides important clues for target
selection in drug discovery and the design of PPI inhibitors. One of the
techniques used for computational prediction of PPIs is protein-protein docking
calculations, and a variety of software has been developed. However, a friendly
interface for users who are not sufficiently familiar with the command line
interface has not been developed so far. In this study, we have developed a
graphical user interface, MEGADOCK-GUI, which enables users to easily predict
PPIs and protein complex structures. In addition to the original 3-D molecular
viewer and input file preparation functions, MEGADOCK-GUI is software that can
automatically perform complete cross-docking of $M$ vs. $N$ proteins. With
MEGADOCK-GUI, various applications related to the prediction of PPIs, such as
ensemble docking that handles multiple conformations of proteins and screening
of binding partner proteins that bind to specific proteins, can now be easily
performed.
| [
{
"created": "Sat, 8 May 2021 07:32:06 GMT",
"version": "v1"
}
] | 2021-05-11 | [
[
"Ohue",
"Masahito",
""
],
[
"Akiyama",
"Yutaka",
""
]
] | Information on protein-protein interactions (PPIs) not only advances our understanding of molecular biology but also provides important clues for target selection in drug discovery and the design of PPI inhibitors. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. However, a friendly interface for users who are not sufficiently familiar with the command line interface has not been developed so far. In this study, we have developed a graphical user interface, MEGADOCK-GUI, which enables users to easily predict PPIs and protein complex structures. In addition to the original 3-D molecular viewer and input file preparation functions, MEGADOCK-GUI is software that can automatically perform complete cross-docking of $M$ vs. $N$ proteins. With MEGADOCK-GUI, various applications related to the prediction of PPIs, such as ensemble docking that handles multiple conformations of proteins and screening of binding partner proteins that bind to specific proteins, can now be easily performed. |
2004.05715 | Luis Pedro Lombardi Junior | Hyun Mo Yang, Luis Pedro Lombardi Junior and Ariana Campos Yang | Modeling the transmission of new coronavirus in S\~ao Paulo State,
Brazil -- Assessing epidemiological impacts of isolating young and elder
persons | 33 pages, 25 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We developed a mathematical model to describe the transmission of new
coronavirus in the S\~ao Paulo State, Brazil. The model divided a community in
subpopulations comprised by young and elder persons, in order to take into
account higher risk of fatality among elder persons with severe CoViD-19. From
data collected in the S\~ao Paulo State, we estimated the transmission and
additional mortality rates, from which we calculated the basic reproduction
number R0. From estimated parameters, estimation of the deaths due to CoViD-19
was three times lower than those found in literature. Considering isolation as
a control mechanism, we varied isolation rates of young and elder persons in
order to assess their epidemiological impacts. The epidemiological scenarios
focused mainly on evaluating the number of severe CoViD-19 cases and deaths due
to this disease when isolation is introduced in a population.
| [
{
"created": "Sun, 12 Apr 2020 23:15:31 GMT",
"version": "v1"
}
] | 2020-04-14 | [
[
"Yang",
"Hyun Mo",
""
],
[
"Junior",
"Luis Pedro Lombardi",
""
],
[
"Yang",
"Ariana Campos",
""
]
] | We developed a mathematical model to describe the transmission of new coronavirus in the S\~ao Paulo State, Brazil. The model divided a community in subpopulations comprised by young and elder persons, in order to take into account higher risk of fatality among elder persons with severe CoViD-19. From data collected in the S\~ao Paulo State, we estimated the transmission and additional mortality rates, from which we calculated the basic reproduction number R0. From estimated parameters, estimation of the deaths due to CoViD-19 was three times lower than those found in literature. Considering isolation as a control mechanism, we varied isolation rates of young and elder persons in order to assess their epidemiological impacts. The epidemiological scenarios focused mainly on evaluating the number of severe CoViD-19 cases and deaths due to this disease when isolation is introduced in a population. |
2406.11975 | Christoph Feinauer | Luca Alessandro Silva, Barthelemy Meynard-Piganeau, Carlo Lucibello,
Christoph Feinauer | Uncovering sequence diversity from a known protein structure | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | We present InvMSAFold, a method for generating a diverse set of protein
sequences that fold into a single structure. For a given structure, InvMSAFold
defines a probability distribution over the space of sequences, capturing the
amino acid covariances observed in Multiple Sequence Alignments (MSA) of
homologous proteins. This allows for the generation of highly diverse protein
sequences while preserving structural and functional integrity. We show that
the higher diversity of sampled sequences translates into higher diversity in
biochemical properties, pointing to exciting prospects for the applicability of
our method in fields like protein design by providing diverse starting points.
| [
{
"created": "Mon, 17 Jun 2024 18:01:03 GMT",
"version": "v1"
}
] | 2024-06-19 | [
[
"Silva",
"Luca Alessandro",
""
],
[
"Meynard-Piganeau",
"Barthelemy",
""
],
[
"Lucibello",
"Carlo",
""
],
[
"Feinauer",
"Christoph",
""
]
] | We present InvMSAFold, a method for generating a diverse set of protein sequences that fold into a single structure. For a given structure, InvMSAFold defines a probability distribution over the space of sequences, capturing the amino acid covariances observed in Multiple Sequence Alignments (MSA) of homologous proteins. This allows for the generation of highly diverse protein sequences while preserving structural and functional integrity. We show that the higher diversity of sampled sequences translates into higher diversity in biochemical properties, pointing to exciting prospects for the applicability of our method in fields like protein design by providing diverse starting points. |
0805.0484 | Denis Semenov A. | Denis A. Semenov | Evolution of the genetic code. From the CG- to the CGUA-alphabet, from
RNA double helix to DNA | 19 pages, 7 tables | null | null | null | q-bio.BM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A hypothesis of the evolution of the genetic code is proposed, the leading
mechanism of which is the nucleotide spontaneous damage leading to
AT-enrichment of the genome. The hypothesis accounts for stability of the
genetic code towards point mutations, the presence of code dialects, emergence
of stop codons, emergence of the DNA double helix and the symmetry of the
genetic code table. The assumption of the originally triplet structure of the
genetic code has been substantiated. A hypothesis concerning the primary
structure of the first gene and the first protein has been proposed.
| [
{
"created": "Mon, 5 May 2008 08:57:51 GMT",
"version": "v1"
}
] | 2008-05-06 | [
[
"Semenov",
"Denis A.",
""
]
] | A hypothesis of the evolution of the genetic code is proposed, the leading mechanism of which is the nucleotide spontaneous damage leading to AT-enrichment of the genome. The hypothesis accounts for stability of the genetic code towards point mutations, the presence of code dialects, emergence of stop codons, emergence of the DNA double helix and the symmetry of the genetic code table. The assumption of the originally triplet structure of the genetic code has been substantiated. A hypothesis concerning the primary structure of the first gene and the first protein has been proposed. |
1711.04852 | Christopher Miles | Christopher E. Miles, Sean D. Lawley, James P. Keener | Analysis of non-processive molecular motor transport using renewal
reward theory | updated to final journal version | SIAM J. Appl. Math. (2018) 78:5, 2511-2532 | 10.1137/17M1156824 | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose and analyze a mathematical model of cargo transport by
non-processive molecular motors. In our model, the motors change states by
random discrete events (corresponding to stepping and binding/unbinding), while
the cargo position follows a stochastic differential equation (SDE) that
depends on the discrete states of the motors. The resulting system for the
cargo position is consequently an SDE that randomly switches according to a
Markov jump process governing motor dynamics. To study this system we (1) cast
the cargo position in a renewal theory framework and generalize the renewal
reward theorem and (2) decompose the continuous and discrete sources of
stochasticity and exploit a resulting pair of disparate timescales. With these
mathematical tools, we obtain explicit formulas for experimentally measurable
quantities, such as cargo velocity and run length. Analyzing these formulas
then yields some predictions regarding so-called non-processive clustering, the
phenomenon that a single motor cannot transport cargo, but that two or more
motors can. We find that having motor stepping, binding, and unbinding rates
depend on the number of bound motors, due to geometric effects, is necessary
and sufficient to explain recent experimental data on non-processive motors.
| [
{
"created": "Mon, 13 Nov 2017 21:06:41 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Sep 2018 17:50:19 GMT",
"version": "v2"
}
] | 2018-09-28 | [
[
"Miles",
"Christopher E.",
""
],
[
"Lawley",
"Sean D.",
""
],
[
"Keener",
"James P.",
""
]
] | We propose and analyze a mathematical model of cargo transport by non-processive molecular motors. In our model, the motors change states by random discrete events (corresponding to stepping and binding/unbinding), while the cargo position follows a stochastic differential equation (SDE) that depends on the discrete states of the motors. The resulting system for the cargo position is consequently an SDE that randomly switches according to a Markov jump process governing motor dynamics. To study this system we (1) cast the cargo position in a renewal theory framework and generalize the renewal reward theorem and (2) decompose the continuous and discrete sources of stochasticity and exploit a resulting pair of disparate timescales. With these mathematical tools, we obtain explicit formulas for experimentally measurable quantities, such as cargo velocity and run length. Analyzing these formulas then yields some predictions regarding so-called non-processive clustering, the phenomenon that a single motor cannot transport cargo, but that two or more motors can. We find that having motor stepping, binding, and unbinding rates depend on the number of bound motors, due to geometric effects, is necessary and sufficient to explain recent experimental data on non-processive motors. |
0708.1971 | Eduardo Candelario-Jalil | E. Candelario-Jalil, H. H. Ajamieh, S. Sam, G. Martinez, O. S. Leon | Nimesulide limits kainate-induced oxidative damage in the rat
hippocampus | null | European Journal of Pharmacology 390(3): 295-298 (2000) | null | null | q-bio.TO | null | Kainate induces a marked expression of cyclooxygenase-2 after its systemic
administration. Because cyclooxygenase-2 activity is associated to the
production of reactive oxygen species, we investigated the effects of
nimesulide, a selective cyclooxygenase-2 inhibitor, on kainate-induced in vivo
oxidative damage in the rat hippocampus. A clinically relevant dose of
nimesulide (6 mg/kg, i.p.) was administered three times following kainate
application (9 mg/kg, i.p.). After 24 h of kainate administration, the drastic
decrease in hippocampal glutathione content and the significant increase in
lipid peroxidation were attenuated in nimesulide-treated rats, suggesting that
the induction of cyclooxygenase-2 is involved in kainate-mediated free radicals
formation.
| [
{
"created": "Tue, 14 Aug 2007 22:30:36 GMT",
"version": "v1"
}
] | 2007-08-16 | [
[
"Candelario-Jalil",
"E.",
""
],
[
"Ajamieh",
"H. H.",
""
],
[
"Sam",
"S.",
""
],
[
"Martinez",
"G.",
""
],
[
"Leon",
"O. S.",
""
]
] | Kainate induces a marked expression of cyclooxygenase-2 after its systemic administration. Because cyclooxygenase-2 activity is associated to the production of reactive oxygen species, we investigated the effects of nimesulide, a selective cyclooxygenase-2 inhibitor, on kainate-induced in vivo oxidative damage in the rat hippocampus. A clinically relevant dose of nimesulide (6 mg/kg, i.p.) was administered three times following kainate application (9 mg/kg, i.p.). After 24 h of kainate administration, the drastic decrease in hippocampal glutathione content and the significant increase in lipid peroxidation were attenuated in nimesulide-treated rats, suggesting that the induction of cyclooxygenase-2 is involved in kainate-mediated free radicals formation. |
1209.2616 | J. C. Phillips | J. C. Phillips | Punctuated evolution of influenza virus neuraminidase (A/H1N1) under
migration and vaccination pressures | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influenza virus contains two highly variable envelope glycoproteins,
hemagglutinin (HA) and neuraminidase (NA). The structure and properties of HA,
which is responsible for binding the virus to the cell that is being infected,
change significantly when the virus is transmitted from avian or swine species
to humans. Here we focus on much smaller human individual evolutionary amino
acid mutational changes in NA, which cleaves sialic acid groups and is required
for influenza virus replication. We show that very small amino acid changes can
be monitored very accurately across many Uniprot and NCBI strains using
hydropathicity scales to quantify the roughness of water film packages.
Quantitative sequential analysis is most effective with the differential
hydropathicity scale based on protein self-organized criticality (SOC). NA
exhibits punctuated evolution at the molecular scale, millions of times smaller
than the more familiar species scale, and thousands of times smaller than the
genomic scale. Our analysis shows that large-scale vaccination programs have
been responsible for a very large convergent reduction in influenza severity in
the last century, a reduction which is hidden from short-term studies of
vaccine effectiveness. Hydropathic analysis is capable of interpreting and even
predicting trends of functional changes in mutation prolific viruses.
| [
{
"created": "Thu, 30 Aug 2012 21:01:32 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Sep 2012 19:51:50 GMT",
"version": "v2"
}
] | 2012-09-14 | [
[
"Phillips",
"J. C.",
""
]
] | Influenza virus contains two highly variable envelope glycoproteins, hemagglutinin (HA) and neuraminidase (NA). The structure and properties of HA, which is responsible for binding the virus to the cell that is being infected, change significantly when the virus is transmitted from avian or swine species to humans. Here we focus on much smaller human individual evolutionary amino acid mutational changes in NA, which cleaves sialic acid groups and is required for influenza virus replication. We show that very small amino acid changes can be monitored very accurately across many Uniprot and NCBI strains using hydropathicity scales to quantify the roughness of water film packages. Quantitative sequential analysis is most effective with the differential hydropathicity scale based on protein self-organized criticality (SOC). NA exhibits punctuated evolution at the molecular scale, millions of times smaller than the more familiar species scale, and thousands of times smaller than the genomic scale. Our analysis shows that large-scale vaccination programs have been responsible for a very large convergent reduction in influenza severity in the last century, a reduction which is hidden from short-term studies of vaccine effectiveness. Hydropathic analysis is capable of interpreting and even predicting trends of functional changes in mutation prolific viruses. |
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